近期论文
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Selected Refereed Journals (Sorted by Reverse Chronological Order)
H.M. Cai, X.Q. Sheng, G.R. Wu, B. Hu, Y.M. Cheung and J.Z. Chen, “Brain Network Classification for Accurate Detection of Alzhemier’s Disease via Manifold Harmonic Discriminant Analysis”, IEEE Transactions on Neural Networks and Learning Systems, in press, DOI: 10.1109/TNNLS.2023.3301456.
W.M. Mai, J.C. Yao, G. Chen, Y. Zhang, Y.M. Cheung and B. Han, “Server-Client Collaborative Distillation for Federated Reinforcement Learning”, ACM Transactions on Knowledge Discovery from Data, in press, DOI: 10.1145/3604939.
X.G. Meng, Q. Liu, C. Yang, L. Zhou and Y.M. Cheung, “DDGAIN-HCNN: A Novel Deep Learning-Based Robust Dual-Rate Dynamic Data Modeling for Quality Prediction”, IEEE Transactions on Industrial Informatics, in press, DOI: 10.1109/TII.2023.3275700.
C. Yang, Q. Liu, Y. Liu and Y.M. Cheung, “Transfer Dynamic Latent Variable Modeling for Quality Prediction of Multimode Processes”, IEEE Transactions on Neural Networks and Learning Systems (Special Issue: Explainable Representation Learning-based Intelligent Inspection and Maintenance of Complex Systems), in press, DOI: 10.1109/TNNLS.2023.3265762.
S.J. Peng, Y. Fan, Y.M. Cheung, X. Liu, Z. Cui and T.H. Li, “Towards Ef?cient Cross-Modal Anomaly Detection Using Triple-adaptive Network and Bi-quintuple Contrastive Learning”, IEEE Transactions on Emerging Topics in Computational Intelligence, in press, DOI: 10.1109/TETCI.2023.3256466.
Y.Q. Yang, X. Tang, Y.M. Cheung, X.R. Zhang and L.C. Jiao, “SAGN: Semantic-Aware Graph Network for Remote Sensing Scene Classification”, IEEE Transactions on Image Processing, Vol. 32, pp. 1011-1025, January 2023, DOI: 10.1109/TIP.2023.3238310.
W. Huang, Y.T. Zhou, Y.M. Cheung, P. Zhang, Y.F. Zha and M. Pang, “Facial Expression Guided Diagnosis of Parkinson’s Disease via High-quality Data Augmentation”, IEEE Transactions on Multimedia, in press, DOI: 10.1109/TMM.2022.3216961.
Y.C. Tang, S. Wang, C.T. Wang, S.J. Xiang and Y.M. Cheung, “A Highly Robust Reversible Watermarking Scheme Using Embedding Optimization and Rounded Error Compensation”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 33, No. 4, pp. 1593-1609, April 2023, DOI: 10.1109/TCSVT.2022.3216849.
Y.Q. Zhang and Y.M. Cheung, “Graph-based Dissimilarity Measurement for Cluster Analysis of Any-Type-Attributed Data”, IEEE Transactions on Neural Networks and Learning Systems, in press, DOI: 10.1109/TNNLS.2022.3202700.
S. Ye, Q.M. Peng, W.J. Sun, J.M. Xu, Y. Wang, X.G. You and Y.M. Cheung, “Discriminative Suprasphere Embedding for Fine-Grained Visual Categorization”, IEEE Transactions on Neural Networks and Learning Systems, in press, DOI: 10.1109/TNNLS.2022.3202534.
M.K. Li, Y.M. Cheung and Z.K. Hu, “Key Point Sensitive Loss for Long-tailed Visual Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, No. 4, pp. 4812-4825, April, 2023, DOI: 10.1109/TPAMI.2022.3196044 [Source Code].
S.J. Peng, Y. He, X. Liu, Y.M. Cheung, X. Xu and Z. Cui, “Relation-Aggregated Cross-Graph Correlation Learning for Fine-Grained Image-Text Retrieval”, IEEE Transactions on Neural Networks and Learning Systems, in press, DOI: 10.1109/TNNLS.2022.3188569.
X. Liu, Y. He, Y.M. Cheung, X. Xu and N.N. Wang, “Learning Relationship-enhanced Semantic Graph for Fine-grained Image-Text Matching”, IEEE Transactions on Cybernetics, in press, DOI: 10.1109/TCYB.2022.3179020.
H.T. Wu, X. Cao, R.Y. Jia and Y.M. Cheung, “Reversible Data Hiding with Brightness Preserving Contrast Enhancement by Two-dimensional Histogram Modification”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 32, No. 11, pp. 7605-7617, November 2022, DOI: 10.1109/TCSVT.2022.3180007.
X. Liu, J.H. Yi, Y.M. Cheung, X. Xu and Z. Cui, “OMGH: Online Manifold-Guided Hashing for Flexible Cross-modal Retrieval”, IEEE Transactions on Multimedia, in press, DOI: 10.1109/TMM.2022.3166668.
M. Pang, B.H. Wang, S.Y. Huang, Y.M. Cheung and B.H. Wen, “A Unified Framework for Bidirectional Prototype Learning from Contaminated Faces across Heterogeneous Domains”, IEEE Transactions on Information Forensics and Security, Vol. 17, pp. 1544-1557, 2022, DOI: 10.1109/TIFS.2022.3164215.
H.T. Wu, Y.M. Cheung, Z.W. Zhuang, L.L. Xu and J.K. Hu, “Lossless Data Hiding in Encrypted Images Compatible with Homomorphic Processing”, IEEE Transactions on Cybernetics, Vol. 53, No. 6, pp. 3688-3701, June 2023, DOI: 10.1109/TCYB.2022.3163245.
C.E. Yang, Y.M. Cheung, J.L. Ding and K.C. Tan, B. Xue and M.J. Zhang, “Contrastive Learning Assisted Alignment for Partial Domain Adaptation”, IEEE Transactions on Neural Networks and Learning Systems, in press, DOI: 10.1109/TNNLS.2022.3145034.
Z.Q. Zhang, Q.M. Peng, S. C. Fu, W.J. Wang, Y.M. Cheung, Y. Zhao, S.J. Yu and X.G. You, “A Componentwise Approach to Weakly-Supervised Semantic Segmentation Using Dual Feedback Network”, IEEE Transactions on Neural Networks and Learning Systems, in press, DOI: 10.1109/TNNLS.2022.3144194.
X. Tang, Y.Q. Yang, J.J. Ma, Y.M. Cheung, C. Liu, F. Liu, X.R. Zhang, and L.C. Jiao, “Meta-hashing for Remote Sensing Image Retrieval”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, 5615419:1-19, March 2022, DOI: 10.1109/TGRS.2021.3136159.
X.M. Xue, C. Yang, Y. Hu, K. Zhang, Y.M. Cheung, L.Q. Song and K.C. Tan, “Evolutionary Sequential Transfer Optimization for Objective-Heterogeneous Problems”, IEEE Transactions on Evolutionary Computation, Vol. 26, No. 6, pp. 1424-1438, December 2022, DOI: 10.1109/TEVC.2021.3133874.
S.W. Wei, Y.P. Wang and Y.M. Cheung, “A Branch Elimination-based Efficient Algorithm for Large-scale Multiple Longest Common Subsequence Problem”, IEEE Transactions on Knowledge and Data Engineering, Vol. 35, No. 3, pp. 2179-2192, March 2023, DOI: 10.1109/TKDE.2021.3115057.
M. Pang, B.H. Wang, M. Ye, Y.M. Cheung, Y.R. Chen and B.H. Wen, “DisP+V: A Unified Framework for Disentangling Prototype and Variation from Single Sample per Person”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 34, No. 2, pp. 867-881, February 2023, DOI: 10.1109/TNNLS.2021.3103194.
M.K. Li and Y.M. Cheung, “Identity-preserved Complete Face Recovering Network for Partial Face Image”, IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 7, No. 2, pp. 604-609, April 2023, DOI: 10.1109/TETCI.2021.3100646 [Source Code].
Y.Q. Yang, X. Tang, Y.M. Cheung, X.R. Zhang, F. Liu, J.J. Ma and L.C. Jiao, “AR2Det: An Accurate and Real-time Rotational One-Stage Ship Detector in Remote Sensing Images”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, 5605414, pp. 1-14, January 2022, DOI: 10.1109/TGRS.2021.3092433.
F.Q. Gu, H.L. Liu, Y.M. Cheung and M.Y. Zheng, “A Rough-to-Fine Evolutionary Multiobjective Optimization Algorithm”, IEEE Transactions on Cybernetics, Vol. 52, No. 12, pp. 13472-13485, December 2022, DOI:10.1109/TCYB.2021.3081357.
X. Liu, X.Z. Wang and Y.M. Cheung, “FDDH: Fast Discriminative Discrete Hashing for Large-scale Cross-Modal Retrieval”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, No. 11, pp. 6306-6320, November 2022, DOI:10.1109/TNNLS.2021.3076684 [Source Code].
Q.Q. Shi, Y.M. Cheung and J. Lou, “Robust Tensor SVD and Recovery with Rank Estimation”, IEEE Transactions on Cybernetics, Vol. 52, No. 10, pp. 10667-10682, October 2022, DOI:10.1109/TCYB.2021.3067676 [Supplementary Materials, Source Code].
Y.Q. Zhang and Y.M. Cheung, “Learnable Weighting of Intra-attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, No. 7, pp. 3560-3576, July 2022 [DOI:10.1109/TPAMI.2021.3056510, Source Code].
C. Yang, Y.M. Cheung, J.L. Ding and K.C. Tan, “Concept Drift-tolerant Transfer Learning in Dynamic Environments”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, No. 8, pp. 3857-3871, August 2022 [DOI: 10.1109/TNNLS.2021.3054665].
J. Lou and Y.M. Cheung, “An Uplink Communication Efficient Approach to Feature-wise Distributed Sparse Optimization with Differential Privacy”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 10, pp. 4529-4543, October 2021, [DOI:10.1109/TNNLS.2020.3020955].
X. Liu, Y.M. Cheung, Z.K. Hu, Y. He and B.E. Zhong, “Adversarial Tri-Fusion Hashing Network for Imbalanced Cross-Modal Retrieval”, IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 5, No. 4, pp. 607-619, August 2021, [DOI:10.1109/TETCI.2020.3007143].
Y.Q. Zhang and Y.M. Cheung, “A New Distance Metric Exploiting Heterogeneous Interattribute Relationship for Ordinal-and-Nominal-Attribute Data Clustering”, IEEE Transactions on Cybernetics, Vol. 52, No. 2, pp. 758-771, February 2022 [DOI: 10.1109/TCYB.2020.2983073, Source Code].
Y. Zhou, H.P. Lu and Y.M. Cheung, “Probabilistic Rank-One Tensor Analysis with Concurrent Regularizations”, IEEE Transactions on Cybernetics, Vol. 51, No. 7, pp. 3496-3509, July 2021 [DOI: 10.1109/TCYB.2019.2914316, Source Code].
M. Pang, Y.M. Cheung, Q.Q. Shi and M.K. Li, “Iterative Dynamic Generic Learning for Face Recognition from a Contaminated Single Sample per Person”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 4, pp. 1560-1574, April 2021, [DOI: 10.1109/TNNLS.2020.2985099, Source Code].
X. Tang, F.B. Meng, X.G. Zhang, Y.M. Cheung, J.J. Ma, F. Liu and L.C. Jiao, “Hyperspectral Image Classification Based on 3-D Octave Convolution with Spatial-Spectral Attention Network”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, No. 3, pp. 2430-2447, March 2021 [DOI:10.1109/TGRS.2020.3005431].
Y. Zhao, Y.M. Cheung, X.G. You, Q.M. Peng, J.T. Peng, P.P. Yuan and Y.F. Shi, “Hyperspectral Image Classification via Spatial Window-Based Multiview Intact Feature Learning”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, No. 3, pp. 2294-2306, March 2021 [DOI:10.1109/TGRS.2020.3004858].
X. Liu, Z.K. Hu, H.B. Ling and Y.M. Cheung, “MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43, No. 3, pp. 964-981, March 2021, [DOI: 10.1109/TPAMI.2019.2940446, Source Code].
Y. Lu, Y.M. Cheung and Y.Y. Tang, “Self-Adaptive Multi-Prototype-based Competitive Learning Approach: A k-means-type Algorithm for Imbalanced Data Clustering”, IEEE Transactions on Cybernetics, Vol. 51, No. 3, pp. 1598-1612, March 2021, [DOI:10.1109/TCYB.2019.2916196, Source Code].
M. Pang, B.H. Wang, Y.M. Cheung, Y.R. Chen and B.H. Wen, “VD-GAN: A Unified Framework for Joint Prototype and Representation Learning from Contaminated Single Sample per Person”, IEEE Transactions on Information Forensics and Security, Vol. 16, pp. 2246-2259, February 2021, [DOI:10.1109/TIFS.2021.3050055].
Y. Zhou and Y.M. Cheung, “Bayesian Low-Tubal-Rank Robust Tensor Factorization with Multi-Rank Determination”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43, No. 1, pp. 62-76, January 2021, [DOI:10.1109/TPAMI.2019.2923240, Source Code].
Y. Lu, Y.M. Cheung and Y.Y. Tang, “Adaptive Chunk-based Dynamic Weighted Majority for Imbalanced Data Streams with Concept Drift”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 8, pp. 2764-2778, August 2020, [DOI: 10.1109/TNNLS.2019.2951814, Source Code].
J. Lou and Y.M. Cheung, “Robust Low-rank Tensor Minimization via a New Tensor Spectral k-Support Norm”, IEEE Transactions on Image Processing, Vol. 29, No. 1, pp. 2314-2327, 2020, [DOI: 10.1109/TIP.2019.2946445, Supplement].
Y. Lu, Y.M. Cheung and Y.Y. Tang, “Bayes Imbalance Impact Index: A Measure of Class Imbalanced Dataset for Classification Problem”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 9, pp. 3525-3539, September 2020, [DOI: 10.1109/TNNLS.2019.2944962, Source Code].
S.Y. Yi, Z.Y. He, X.Y. Jing, Y. Li, Y.M. Cheung and F.P. Nie, “Adaptive Weighted Sparse Principal Component Analysis for Robust Unsupervised Feature Selection”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 6, pp. 2153-2163, June 2020 [DOI:10.1109/TNNLS.2019.2928755].
M. Pang, Y.M. Cheung, B.H. Wang and J. Lou, “Synergistic Generic Learning for Face Recognition from a Contaminated Single Sample per Person”, IEEE Transactions on Information Forensics and Security, Vol. 15, pp. 195-209, 2020, [DOI:10.1109/TIFS.2019.2919950, Demo Code].
Y.M. Cheung, F.Q. Gu, H.L. Liu, K.C. Tan and H. Huang, “Objective-Domain Dual Decomposition: An Effective Approach to Optimizing Partial Differentiable Objective Functions”, IEEE Transactions on Cybernetics, Vol. 50, No. 3, pp. 923-934, March 2020 [DOI: 10.1109/TCYB.2018.2870487].
Y. Zhou and Y.M. Cheung, “Probabilistic Rank-One Discriminant Analysis via Collective and Individual Variation Modeling”, IEEE Transactions on Cybernetics, Vol. 50, No. 2, pp. 627-639, February 2020 [DOI:10.1109/TCYB.2018.2870440].
Y.Q. Zhang, Y.M. Cheung and K.C. Tan, “A Unified Entropy-Based Distance Metric for Ordinal-and-Nominal-Attribute Data Clustering”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 1, pp. 39-52, January 2020, [DOI: 10.1109/TNNLS.2019.2899381, Source Code].
Q.M. Peng and Y.M. Cheung, “Automatic Video Object Segmentation Based on Visual and Motion Saliency”, IEEE Transactions on Multimedia, Vol. 21, No. 12, pp. 3083-3094, December, 2019 [DOI:10.1109/TMM.2019.2918730].
M. Pang, Y.M. Cheung, B.H. Wang and R.S. Liu, “Robust Heterogeneous Discriminative Analysis for Face Recognition with Single Sample per Person”, Pattern Recognition, Vol. 89, pp. 91-107, 2019 [DOI: 10.1016/j.patcog.2019.01.005, Source Code].
M. Pang, Y.M. Cheung, R.S. Liu, J. Lou and C. Lin, “Toward Efficient Image Representation: Sparse Concept Discriminant Matrix Factorization”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 29, No. 11, pp. 3184-3198, November 2019 [DOI: 10.1109/TCSVT.2018.2879833, Source Code].
S.Y. Yi, Z.Y. He, Y. Li and Y.M. Cheung, “Dual Pursuit for Subspace Learning”, IEEE Transactions on Multimedia, Vol. 21, No. 6, pp. 1399-1411, June 2019 [DOI:10.1109/TMM.2018.2877888].
Q.Q. Shi, Y.M. Cheung, Q.B. Zhao and H.P. Lu, “Feature Extraction for Incomplete Data via Low-rank Tensor Decomposition with Feature Regularization”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, No. 6, pp. 1803-1817, June 2019 [DOI:10.1109/TNNLS.2018.2873655].
L. Chen, H.L. Liu, K.C. Tan, Y.M. Cheung and Y.P. Wang, “Evolutionary Many-Objective Algorithm Using Decomposition Based Dominance Relationship”, IEEE Transactions on Cybernetics, Vol. 49, No. 12, pp. 4129-4139, December, 2019 [DOI: 10.1109/TCYB.2018.2859171].
Y.M. Cheung and Y.Q. Zhang, “Fast and Accurate Hierarchical Clustering Based on Growing Multi-Layer Topology Training”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, No. 3, pp. 876-890, March 2019 [DOI: 10.1109/TNNLS.2018.2853407, Source Code].
X. Liu, J.J. Geng, H.B. Ling and Y.M. Cheung, “Attention Guided Deep Audio-face Fusion for Efficient Speaker Naming”, Pattern Recognition, Vol. 88, pp. 557-568, 2018.
Q.Q. Shi, H.P. Lu and Y.M. Cheung, “Rank-One Matrix Completion with Automatic Rank Estimation via L1-Norm Regularization”, IEEE Transactions on Neural Networks and Learning Systems, 29(10):4744-4757, October 2018 [DOI: 10.1109/TNNLS.2017.2766160, Source Code].
J.B. Zeng, Y. Liu, B. Leng, Z. Xiong and Y.M. Cheung, “Dimensionality Reduction in Multiple Ordinal Regression”, IEEE Transactions on Neural Networks and Learning Systems, 29(9):4088-4101, September 2018 [DOI: 10.1109/TNNLS.2017.2752003].
H. Jia and Y.M. Cheung, “Subspace Clustering of Categorical and Numerical Data With an Unknown Number of Clusters”, IEEE Transactions on Neural Networks and Learning Systems, 29(8):3308-3325, August 2018 [DOI: 10.1109/TNNLS.2017.2728138, Source Code].
F.Q. Gu and Y.M. Cheung, “Self-organizing Map-based Weight Design for Decomposition-based Many-objective Evolutionary Algorithm”, IEEE Transactions on Evolutionary Computation, 22(2):211-225, March 2018 [DOI: 10.1109/TEVC.2017.2695579].
Y. Wang, J.W. Wan, J. Guo, Y.M. Cheung and P.C. Yuen, “Inference-based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(7):1611-1624, July 2018 [DOI: 10.1109/TPAMI.2017.2727048].
S.Y. Yi, Z.Y. He, Y.M. Cheung and W.S. Chen, “Unified Sparse Subspace Learning via Self-contained Regression”, IEEE Transactions on Circuits and Systems for Video Technology, 28(10):2537-2550, October 2018 [DOI: 10.1109/TCSVT.2017.2721541].
Y. Zhang, Y.M. Cheung, B. Xu and W.F. Su, “Detection Copy Number Variants from NGS with Sparse and Smooth Constraints”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(4):856-867, August 2017 [DOI: 10.1109/TCBB.2016.2561933].
X. Liu, H. Zhang, Y.M. Cheung, X.G. You and Y.Y. Tang, “Efficient Single Image Dehazing and Denosing: An Efficient Multi-scale Correlated Wavelet Approach”, Computer Vision and Image Understanding, Vol. 162, pp. 23-33, 2017 [DOI: 10.1016/j.cviu.2017.08.002] [Source Code].
X. Liu, G.F. He, S.J. Peng, Y.M. Cheung and Y.Y. Tang, “Efficient Human Motion Retrieval via Temporal Adjacent Bag of Words and Discriminative Neighborhood Preserving Dictionary Learning”, IEEE Transactions on Human-Machine System, 47(6):763-776, 2017 [DOI: 10.1109/THMS.2017.2675959].
Y.M. Cheung and J. Lou, “Proximal Average Approximated Incremental Gradient Descent for Composite Penalty Regularized Empirical Risk Minimization”, Machine Learning, 106(4):595-622, April 2017 [DOI: 10.1007/s10994-016-5609-1].
Y.M. Cheung, M. Li, Q.M. Peng and C.L. Philip Chen, “A Cooperative Learning-based Clustering Approach to Lip Segmentation without Knowing Segment Number”, IEEE Transactions on Neural Networks and Learning Systems, 28(1):80-93, January 2017 [DOI: 10.1109/TNNLS.2015.2501547].
Z.Y. He, S.Y. Yi, Y.M. Cheung, X.G. You and Y.Y. Tang, “Robust Object Tracking via Key Patch Sparse Representation”, IEEE Transactions on Cybernetics, 47(2):354-364, February 2017 [DOI: 10.1109/TCYB.2016.2514714].
S.Y. Yi, Z.H. Lai, Z.Y. He, Y.M. Cheung and Y. Liu, “Joint Sparse Principal Component Analysis”, Pattern Recognition, Vol. 61, pp. 524-536, 2017 [Source Code].
Q.M. Peng, Y.M. Cheung, X.G. You and Y.Y. Tang, “A Hybrid of Local and Global Saliencies for Detecting Image Salient Region and Appearance”, IEEE Transactions on Systems, Man and Cybernetics: Systems, 47(1):86-97, January 2017 [DOI: 10.1109/TSMC.2016.2564922].
Y.M. Cheung, F.Q. Gu and H.L. Liu, “Objective Extraction for Many-Objective Optimization Problems: Algorithm and Test Problems”, IEEE Transactions on Evolutionary Computation, 20(5): 755-772, October 2016.
H. Jia, Y.M. Cheung and J.M. Liu, “A New Distance Metric for Unsupervised Learning of Categorical Data”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 27, No. 5, pp. 1065-1079, 2016 [Source Code].
X. Liu, Y.M. Cheung and Y.Y. Tang, “Lip Event Detection Using Oriented Histograms of Regional Optical Flow and Low Rank Affinity Pursuit”, Computer Vision and Image Understanding, Vol. 148, pp. 153-163, 2016 [DOI: 10.1016/j.cviu.2015.11.015].
P.Y. Zhang, X.G. You, W.H. Ou, C.L. Philip Chen and Y.M. Cheung, “Sparse Discriminative Multi-manifold Embedding for One-sample Face Identification”, Patter Recognition, Vol. 52, pp. 249-259, April 2016.
Z.Q. Zhao, Y.M. Cheung, H.B. Hu, and X.D. Wu, “Corrupted and Occluded Face Recognition via Cooperative Sparse Representation”, Pattern Recognition, Vol. 56, pp. 77-87, August 2016.
Y. Wang, L.P. Wang, Y.M. Cheung and P.C. Yuen, “Learning Compact Binary Codes for Hash-based Fingerprint Indexing”, IEEE Transactions on Information Forensics and Security, 10(8), pp. 1603-1616, 2015.
Y.M. Cheung and Q.M. Peng, “Eye Gaze Tracking with a Web Camera in a Desktop Environment”, IEEE Transactions on Human-Machine Systems, 45(4): 419-430, 2015.
Y.M. Cheung, M. Li, X. Cao and X.G. You, "Lip Segmentation under MAP-MRF Framework with Automatic Selection of Local Observation Scale and Number of Segments", IEEE Transactions on Image Processing, Vol. 23, No. 8, pp. 3397-3411, 2014.
X. Liu and Y.M. Cheung, "Learning Multi-Boosted HMMs for Lip-Password Based Speaker Verification", IEEE Transactions on Information Forensics and Security, Vol. 9, No. 2, pp. 233-246, 2014.
H.Jia, Y.M. Cheung and J. Liu, "Cooperative and Penalized Competitive Learning with Application to Kernel-based Clustering", Pattern Recognition, Vol. 47, pp. 3060-3069, 2014.
M. Lutf, X.G. You, Y.M. Cheung and C.L. Philip Chen, “Arabic Font Recognition Based on Diacritics Features”, Pattern Recognition, Vol. 47, No. 2, pp. 672-684, 2014.
Y.M. Cheung and H. Jia, "Categorical-and-numerical-attribute Data Clustering based on a Unified Similarity Metric without Knowing Cluster Number", Pattern Recognition, Vol. 46, No. 8, pp. 2228-2238, 2013. [Source Code (zip)]
H. Zeng and Y.M. Cheung, "Semi-supervised Maximum Margin Clustering with Pairwise Constraints", IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 5, pp. 926-939, 2012.
Y.M. Cheung, X. Liu and X.G. You, "A Local Region Based Approach to Lip Tracking", Pattern Recognition, Vol. 45, No. 9, pp. 3336-3347, 2012.
H. Zeng and Y.M. Cheung, "Feature Selection and Kernel Learning for Local Learning-Based Clustering", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 8, pp. 1532-1547, August 2011.
X.G. You, L. Du, Y.M. Cheung and Q.H. Chen, "A Blind Watermarking Scheme Using New Nontensor Product Wavelet Filter Banks", IEEE Transactions on Image Processing, Vol. 19, No. 12, pp. 3271-3284, 2010.
Y. Liu, Mark Li, Y.M. Cheung, Pak C. Sham and Michael K. Ng, "SKM-SNP: SNP Markers Detection Method", Journal of Biomedical Informatics, Vol. 43, No. 2, pp. 233-239, 2010.
H.T. Wu and Y.M. Cheung, "Reversible Watermarking by Modulation and Security Enhancement", IEEE Transactions on Instrumentation and Measurement, Vol. 59, No. 1, pp. 221-228, 2010.
Y.M. Cheung and H. Zeng, "Local Kernel Regression Score for Selecting Features of High-dimensional Data", IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 12, pp. 1798-1802, 2009.
H. Zeng and Y.M. Cheung, "A New Feature Selection Method for Gaussian Mixture Clustering", Pattern Recognition, Vol. 42, No. 2, pp. 243-250, 2009.
Mark J. Li, Michael K. Ng, Y.M. Cheung and Joshua Z.X. Huang, "Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters", IEEE Transactions on Knowledge and Data Engineering, Vol. 20, No. 11, pp. 1519-1534, 2008.
Y.M. Cheung and H.T. Wu, "A Sequential Quantization Strategy for Data Embedding and Integrity Verification", IEEE Transactions on Circuits and Systems for Video Technology, Vol. 17, No. 8, pp. 1007-1016, 2007.
Z.Y. Zhang and Y.M. Cheung, "On Weight Design of Maximum Weighted Likelihood and an Extended EM Algorithm", IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 10, pp. 1429-1434, 2006.
Y.M. Cheung, "Maximum Weighted Likelihood via Rival Penalized EM for Density Mixture Clustering with Automatic Model Selection", IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6, pp. 750--761, 2005. [Source Code (zip)]
Y.M. Cheung, "On Rival Penalization Controlled Competitive Learning for Clustering with Automatic Cluster Number Selection", IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 11, pp. 1583-1588, 2005.
X.M. Zhao, Y.M. Cheung and D.S. Huang, "A Novel Approach to Extracting Features from Motif Content and Protein Composition for Protein Sequence Classification", Neural Networks, Vol. 18, No. 8, pp. 1019-1028, October, 2005.
Y.M. Cheung , "k*-Means: A New Generalized k-Means Clustering Algorithm", Pattern Recognition Letters, Vol. 24, Issue 15, pp. 2883--2893, 2003.
Y.M. Cheung and L. Xu, "Dual Multivariate Auto-regressive Modeling in State Space for Temporal Signal Separation", IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, Vol. 33, No. 3, pp. 386-398, June, 2003.
K.K. Hung, Y.M. Cheung and L. Xu, "An Extended ASLD Trading System to Enhance Portfolio Management", IEEE Transactions on Neural Networks, Vol. 14, No. 2, pp. 413--425, March, 2003.
Y.M. Cheung and L. Xu, "An RPCL-based Approach for Markov Model Identification with Unknown State Number", IEEE Signal Processing Letters, Vol. 7, No. 10, pp. 284-287, October, 2000.
Selected Refereed Conference Papers (Sorted by Reverse Chronological Order)
S.X. Li, L.Y. Song, Y.M. Cheung and X. Yao, “BEDCOE: Borderline Enhanced Disjunct Cluster Based Oversampling Ensemble for Online Multi-class Imbalance Learning”, Proceedings of the 26th European Conference on Artificial Intelligence (ECAI’2023), Krakow, Poland, September 30 – October 4, 2023.
Y. Lu, Y.L. Zhang, B. Han, Y.M. Cheung and H.Z. Wang, “Label-Noise Learning with Intrinsically Long-Tailed Data”, Proceedings of 2023 IEEE/CVF International Conference on Computer Vision (ICCV’2023), Paris, France, October 2-6, 2023, in press.
Z.K. Hu, Y.M. Cheung, M.K. Li and W.C. Lan, “Few-shot Lip-password Based Speaker Verification”, Proceedings of 2023 International Conference on Image Processing (ICIP’2023), Kuala Lumpur, Malaysia, October 8-11, 2023, in press.
S.X. Li, L.Y. Song, X.Y. Wu, Z. Hu, Y.M. Cheung and X. Yao, “ARConvL: Adaptive Region-Based Convolutional Learning for Multi-class Imbalance Classification”, Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2023 (ECML-PKDD’23), Turin, Italy, September 18-22, 2023, in press.
X.T. Jiang, X. Liu, Y.M. Cheung, X. Xu, S.K. Zheng and T.H. Li, “Label-Semantic-Enhanced Online Hashing for Efficient Cross-modal Retrieval”, Proceedings of IEEE International Conference on Multimedia and Expo (ICME’23), Brisbane, Australia, July 10-14, 2023, in press.
Y. Jin, M.K. Li, Y. Lu, Y.M. Cheung and H.Z. Wang, “Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation”, Proceedings of the Thirty-Fourth IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR’2023), pp. 23695-23704, Vancouver, Canada, June 18-22, 2023 [Paper].
Z.N. Yu, X. Liu, Y.M. Cheung, M.H. Zhu, X. Xu, N.N. Wang and T.H. Li, “Detach and Enhance: Learning Disentangled Cross-modal Latent Representation for Efficient Face-Voice Association and Matching”, Proceedings of the 22nd IEEE International Conference on Data Mining (ICDM’2022), pp. 648-655, Orlando, FL, USA, November 28-December 1, 2022 [Paper, Regular Paper with the acceptance rate: 85 regular papers / 870 total submissions ? 9.77%].
M. Pang, B.H. Wang, S.B. Chen, Y.M. Cheung, R. Zou and W. Huang, “Cross-domain Prototype Learning from Contaminated Faces via Disentangling Latent Factors”, Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM’2022), pp. 4369-4373, Atlanta, Georgia, USA, October 17-21, 2022 [Paper].
X.Y. Shang, Y. Lu, Y.M. Cheung and H.Z. Wang, “FEDIC: Federated Learning on Non-IID and Long-tailed Data via Calibrated Distillation”, Proceedings of IEEE International Conference on Multimedia and Expo 2022 (ICME’22), Taipei, Taiwan, July18-22, 2022, DOI: 10.1109/ICME52920.2022.9860009 [Paper].
M.K. Li, Y.M. Cheung and J.Y. Jiang, “Feature-balanced Loss Long-tailed Visual Recognition”, Proceedings of IEEE International Conference on Multimedia and Expo 2022 (ICME’22), Taipei, Taiwan, July18-22, 2022, DOI: 10.1109/ICME52920.2022.9860003 [Paper].
Y.Q. Zhang, Y.M. Cheung and A. Zeng, “Het2Hom: Representation of Heterogeneous Attributes into Homogeneous Concept Spaces for Categorical-and-Numerical-Attribute Data Clustering”, Proceedings of the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI’22), pp. 3758-3765, Vienna, Austria, July 23-29, 2022 [Paper, Source Code].
M.K. Li, Y.M. Cheung and Y. Lu, “Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment”, Proceedings of 2022 Conference on Computer Vision and Pattern Recognition (CVPR’2022), pp. 6929-6938, New Orleans, Louisiana, USA, June 19-24, 2022 [Paper, Source Code].
M.K. Li, Y.M. Cheung, and J.Y. Jiang, “Feature-balanced Loss Long-tailed Visual Recognition”, Proceedings of IEEE International Conference on Multimedia and Expo 2022 (ICME’22), Taipei, Taiwan, July18-22, 2022, in press [Source Code].
Xinyi Shang, Y. Lu, Y.M. Cheung and H.Z. Wang, “FEDIC: Federated Learning on Non-IID and Long-tailed Data via Calibrated Distillation”, Proceedings of IEEE International Conference on Multimedia and Expo 2022 (ICME’22), Taipei, Taiwan, July18-22, 2022, in press.
S.W. Wei, Y.P. Wang and Y.M. Cheung, “A Branch Elimination-based Efficient Algorithm for Large-scale Multiple Longest Common Subsequence Problem”, Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE’2022), pp. 1485-1486, Kuala Lumpur, Malaysia, May 9-12, 2022.
Y. Yu, M.T. Wu, W.F. Su and Y.M. Cheung, “A Scoring Model Assisted by Frequency for Multi-Document Summarization”, Proceedings of the 30th International Conference on Artificial Neural Networks (ICANN’2021), LNCS 12895, pp. 309-320, September 14-17, 2021 [Paper].
Y. He, X. Liu, Y.M. Cheung, S.J. Peng, J.H. Yi and W.T. Fan, “Cross-Graph Attention Enhanced Multi-Modal Correlation Learning for Fine-Grained Image-Text Retrieval”, Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 21), pp. 1865-1869, July 11-15, 2021 [Paper].
Y.X. Huang, X. Cao, H.T. Wu and Y.M. Cheung, “Reversible Data Hiding in JPEG Images for Privacy Protection”, Proceedings of 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’2021), pp. 2715-2719, Toronto, Ontario, Canada, June 6-11, 2021 [Paper].
Y.M. Cheung, M.K. Li and R. Zou, “Facial Structure Guided GAN for Identity-preserved Face Image De-occlusion”, Proceeding of ACM International Conference on Multimedia Retrieval 2021 (ICMR’2021), pp. 46-54, Taipei, Taiwan, August 21-24, 2021 [Paper, Source Code].
J. Lou and Y.M. Cheung, “Projection-free Online Empirical Risk Minimization with Privacy-preserving and Privacy Expiration”, in Proceedings of 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’20), December 14-17, 2020, DOI: 10.1109/WIIAT50758.2020.00006 (Best in Theoretical Paper Award) [Paper].
K. Cheng, X. Liu, Y.M. Cheung, R. Wang, X. Xu and B.E. Zhong, “Hearing like Seeing: Improving Voice-Face Interactions and Associations via Adversarial Deep Semantic Matching Network”, Proceedings of the 28th ACM International Conference on Multimedia, pp. 448-455, Seattle, USA, October 12-16, 2020, DOI:10.1145/3394171.3413710 [Paper].
R. Wang, X. Liu, Y.M. Cheung, K. Cheng, N.N. Wang and W.T. Fan, “Learning Discriminative Joint Embeddings for Efficient Face and Voice Association”, Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’20), pp. 1881-1884, July 25-30, 2020 [Paper].
M. Pang, Y.M. Cheung, Q.Q. Shi and M.K. Li, “Iterative Dynamic Generic Learning for Single Sample Face Recognition with a Contaminated Gallery”, Proceedings of 2020 IEEE International Conference on Multimedia & Expo (ICME’2020), London, UK, July 6-10, 2020, DOI: 10.1109/ICME46284.2020.9102792 [Paper].
Y.Q. Zhang and Y.M. Cheung, “An Ordinal Data Clustering Algorithm with Automated Distance Learning”, Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI’2020), pp. 6869-6876, New York, USA, February 7-12, 2020 [Paper, Source Code].
Z.K. Hu, X. Liu, X.Z. Wang, Y.M. Cheung, N.N. Wang and Y.W. Chen, “Triplet Fusion Network Hashing for Unpaired Cross-model Retrieval”, Proceedings of the 2019 on International Conference on Multimedia Retrieval (ICMR’2019), pp. 141-149, Ottawa ON, Canada, June 10-13, 2019 [Paper].
X.Z. Wang, X. Liu, S.J. Peng, Yiu-ming Cheung, Z.K. Hu and N.N. Wang, “Fast Semantic Preserving Hashing for Large-Scale Cross-Modal Retrieval”, Proceedings of the 19th IEEE International Conference on Data Mining (ICDM’2019), pp. 1348-1353, Beijing, China, November 8-11, 2019 [Paper].
J. Lou and Y.M. Cheung, “Uplink Communication Efficient Differentially Private Sparse Optimization with Feature-wise Distributed Data”, Proceedings of the Thirty-second AAAI Conference on Artificial Intelligence (AAAI’2018), pp. 125-133, New Orleans, Louisiana, USA, February 2-7, 2018 [Paper].
Y. Liu, Z.L. Gu, Y.M. Cheung and K.A. Hua, “Multi-view Manifold Learning for Media Interestingness Prediction”, Proceedings of ACM International Conference on Multimedia Retrieval (ICMR’2017), pp. 308-314, Bucharest, Romania, June 6-9, 2017 [Paper].
M. Pang, Y.M. Cheung, B.H. Wang and R.S. Liu, “Robust Heterogeneous Discriminative Analysis for Single Sample Per Person Face Recognition”, Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM’17), pp. 2251-2254, Singapore, November 6-10, 2017. [Paper]
Q.Q. Shi, H.P. Lu and Y.M. Cheung, “Tensor Rank Estimation and Completion via CP-based Nuclear Norm”, Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM’17), pp. 949-958, Singapore, November 6-10, 2017. [Paper, Source Code (zip)]
Q.Q. Shi, Y.M. Cheung and Q.B. Zhao, “Feature Extraction for Incomplete Data via Low-rank Tucker Decomposition”, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD’2017), pp. 564-581, Skopje, Macedonia, September 18-22, 2017. [Paper, Source Code (zip)]
Y. Lu, Y.M. Cheung and Y.Y. Tang, “Dynamic Weighted Majority for Incremental Learning of Imbalanced Data Streams with Concept Drift”, Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’2017), pp. 2393-2399, Melbourne, Australia, August 19-25, 2017. [Paper, Source Code (zip)]
Y. Zhou, H.P. Lu and Y.M. Cheung, “Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations”, Proceedings of the Thirty-first AAAI Conference on Artificial Intelligence (AAAI’2017), pp. 2949-2955, San Francisco, California USA, February 4-9, 2017. [Paper]
Y.M. Cheung and J. Lou, “Scalable Spectral k-Support Norm Regularization for Robust Low Rank Subspace Learning”, Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM’2016), pp. 1151-1160, Indianapolis, USA, October 24-28, 2016, DOI: http://dx.doi.org/10.1145/2983323.2983738 [Paper, Source Code].
H.F. Huang, H. Zhang and Y.M. Cheung, “The Common Self-Polar Triangle of Separate Circles: Properties and Applications to Camera Calibration”, Proceedings of IEEE International Conference on Image Processing (ICIP’2016), pp. 1170-1174, Phoenix, Arizona, USA, September 25-28, 2016. [Paper]
Y. Lu, Y.M. Cheung and Y.Y. Tang, “Hybrid Sampling with Bagging for Class Imbalance Learning”, Proceedings of the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’2016), LNAI 9651, Part I, pp. 14-26, Auckland, New Zealand, April 19-22, 2016. [Paper]
H.F. Huang, H. Zhang and Y.M. Cheung, “Homography Estimation from the Common Self-polar Triangle of Separate Ellipses”, Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’2016), pp. 1737-1744, 2016. [Paper]
Y.M. Cheung and J. Lou, “Efficient Generalized Conditional Gradient with Gradient Sliding for Composite Optimization”, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI’2015), pp. 3409-3415, 2015. [Paper]
H.F. Huang, H. Zhang and Y.M. Cheung, “The Common Self-polar Triangle of Concentric Circles and Its Application to Camera Calibration”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’2015), pp. 4065-4072, 2015. [Paper]
Y.M. Cheung and J. Lou, “Proximal Average Approximated Incremental Gradient Method for Composite Penalty Regularized Empirical Risk Minimization”, JMLR: Workshop and Conference Proceedings of the 7th Asian Conference on Machine Learning (ACML’2015), 45, pp. 205-220, Hong Kong, November 20-22, 2015. [Paper]
Z.Q. Zhao, B.J. Xie, Y.M. Cheung and X.D. Wu, “Plant Leaf Identification via A Growing Convolution Neural Network with Progressive Sample Learning”, Proceedings of the 12th Asian Conference on Computer Vision (ACCV’14), Part II, LNCS 9004, pp. 348-361, Singapore, November 1-5, 2014.
H.F. Huang, H. Zhang and Y.M. Cheung, “Camera Calibration Based on the Common Self-polar Triangle of Sphere Images”, Proceedings of the 12th Asian Conference on Computer Vision (ACCV’14), Part II, LNCS 9004, pp. 19-29, Singapore, November 1-5, 2014. [Paper]
J. Wang, Z.Q. Zhao, X.G. Hu, Y.M. Cheung, M. Wang and X.D. Wu, “Online Group Feature Selection”, Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI’2013), pp. 1757-1763, Beijing, August 2013. [Paper]
Y.M. Cheung and H. Jia, “A Unified Metric for Categorical and Numerical Attributes in Data Clustering”, Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’2013), Vol. 2, LNAI 7819, pp. 135-146, April 14-17, Gold Coast, Australia, 2013.
X. Liu and Y.M. Cheung, “A Multi-boosted HMM Approach to Lip Password Based Speaker Verification”, Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’2012), pp. 2197-2200, Kyoto, Japan, March 25-30, 2012.
S.J. Peng, X. Liu and Y.M. Cheung, “Subspace Based Active Contours with a Joint Distribution Metric for Semi-supervised Natural Image Segmentation”, Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’2012), pp. 1173-1176, Kyoto, Japan, March 25-30, 2012.
Y.M. Cheung and M. Li, “MAP-MRF Based Lip Segmentation without True Segment Number”, Proceedings of 19th IEEE International Conference on Image Processing (ICIP’11), pp. 781-784, September 11-14, 2011.
X. Liu and Y.M. Cheung, "A Robust Lip Tracking Algorithm Using Localized Color Active Contours and Deformable Models", Proceedings of 2011 International Conference on Acoustics, Speech and Signal Processing (ICASSP'11), pp. 1197-1200, 2011. [Paper]
S.J. Peng, X. Liu and Y.M. Cheung, “Active Contours with a Novel Distribution Metric for Complex Object Segmentation”, Proceedings of 19th IEEE International Conference on Image Processing (ICIP’11), pp. 3406-3409, September 11-14, 2011.
Y.M. Cheung and H. Jia, "A Cooperative and Penalized Competitive Learning Approach to Gaussian Mixture Clustering", Proceedings of the 20th International Conference on Artificial Neural Networks (ICANN'10), Lecture Notes in Computer Science (LNCS 6354), pp. 435-440, 2010. [Paper]
M. Li and Y.M. Cheung, "Automatic Segmentation of Color Lip Images Based on Morphological Filter", Proceedings of the 20th International Conference on Artificial Neural Networks (ICANN'10), Lecture Notes in Computer Science (LNCS 6352), pp. 384-387, 2010. [Paper]
X. Liu, Y.M. Cheung, M. Li and H.L. Liu, "A Lip Contour Extraction Method Using Localized Active Contour Model with Automatic Parameter Selection", Proceedings of 2010 International Conference on Pattern Recognition (ICPR'2010), pp. 4332-4335, 2010. [Paper]
Q.M. Peng, X.G. You, L. Zhou and Y.M. Cheung, "Retinal Blood Vessels Segmentation Using the Radial Projection and Supervised Classification", Proceedings of 2010 International Conference on Pattern Recognition (ICPR'2010), pp. 1489-1492, 2010. [Paper]
P. Winoto and Y.M. Cheung and J.M. Liu, "Mechanism Design for Clustering Aggregation by Selfish Systems", Proceedings of 7th IEEE International Conference on Data Mining (ICDM'07), pp. 703-708, 2007. [Paper]
Y.M. Cheung and H. Zeng, "A Maximum Weighted Likelihood Approach to Simultaneous Model Selection and Feature Weighting in Gaussian Mixture", Proceedings of 2007 International Conference on Artificial Neural Networks (ICANN'07), LNCS 4668, Part I: pp. 78-87, 2007. [Paper]
H.T. Wu and Y.M. Cheung, "A High-Capacity Data Hiding Method for Polygonal Meshes", Proceedings of 8th Information Hiding (IH'06), 2006. [Paper]
H.T. Wu and Y.M. Cheung, "A Fragile Watermarking Scheme for 3D Meshes", Proceedings of the 7th Workshop on Multimedia & Security (ACM'05), pp. 117-123, 2005. [Paper]
H.L. Liu and Y.M. Cheung, "A Learning Framework for Blind Source Separation Using Generalized Eigenvalues", Proceedings of International Symposium on Neural Network, LNCS 3497, pp. 472--477, Chongqin, 2005. [Paper]
Y.M. Cheung, "A Rival Penalized EM Algorithm towards Maximizing Weighted Likelihood for Density Mixture Clustering with Automatic Model Selection", Proceedings of the 17th International Conference on Pattern Recognition (ICPR'04), Vol. 4, pp. 633-636, Cambridge, United Kingdom, 2004. [Paper]
Y.M. Cheung, "A Competitive and Cooperative Learning Approach to Robust Data Clustering", Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence (NCI'2004), pp. 131-136, Grindelwald, Switzerland, 2004. [Paper]
Y.M. Cheung and Hailin Liu, "A New Approach to Blind Source Separation with Global Optimal Property", Proceedings of the IASTED International Conference of Neural Networks and Computational Intelligence (NCI'2004), pp. 137-141, Grindelwald, Switzerland, 2004. [Paper]
Y.M. Cheung and R.B. Huang, "An Advance on Divide-and-Conquer Based Radial Basis Function Networks", Proceedings of Fourth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'2003), pp. 143--150, Springer Publisher, Hong Kong, March 21-23, 2003. [Paper]
L.T. Law and Y.M. Cheung, "Color Image Segmentation Using Rival Penalized Controlled Competitive Learning", Proceedings of 2003 International Joint Conference on Neural Networks (IJCNN'2003), Portland, Oregon, USA, July 20-24, 2003. [Paper]
Y.M. Cheung, "Rival Penalization Controlled Competitive Learning for Data Clustering with Unknown Cluster Number", Proceedings of 9th International Conference on Neural Information Processing (Paper ID: 1983 in CD-ROM Proceeding), Singapore, November 18-22, 2002. [Paper]
Y.M. Cheung, "A New Recurrent Radial Basis Function Network", Proceedings of 9th International Conference on Neural Information Processing (Paper ID: 1538 in CD-ROM Proceeding) , Singapore, November 18-22, 2002. [Paper]
Book Chapter
[B.001] Z.B. Lai, Y.M. Cheung and L. Xu, "Independent Component Ordering in ICA Analysis of Financial Data", Computational Finance, Chapter 14, pp. 201-212, edited by Yaser S. Abu-Mostafa, Blake LeBaron, Andrew W. Lo and Andreas S. Weigend, The MIT Press, 1999.
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