个人简介
本人的研究方向聚焦于新一代人工智能技术的前沿领域，主要研究基于低秩模型的高效处理高维数据的方法，致力于解决大数据产业中常见的数据结构复杂、带强噪声、有损毁或缺失等问题，以此克服传统机器学习方法失效的局限性。在多年的研究过程中，本人采用基于低秩三维数组的非凸优化方法，成功攻克了多视数据子空间聚类、高维数据的补全、三维图像显著区域检测以及视频前景背景分离等基础性难题。本人的研究成果在国内外产生了一定的影响，并得到了同行专家的高度评价。相关研究成果已发表于 IEEE T-SP (一作）、SIAM Journal on Imaging Sciences (一作）Neural Networks(通讯）、以及Pattern Recognition (一作+通讯）等权威国际期刊，涉及模式识别和机器学习领域。此外，本人还与多位合作者合作在多个JCR一区刊物上发表了10多篇论文，其中包括Information Sciences、IEEE汇刊T-CYB、T-MM 、T-KDE、T-IP等，被Google Scholar引用近200次。本人的研究成果得到了来自中、美、比利时等多个国家的引用和正面评价。我是《Frontiers》编辑委员会的审稿编辑，专门负责《计算与数据科学数学》的编辑工作（这是《应用数学与统计前沿》的专栏部分）。
My research focuses on the cutting-edge domains of the next-generation artificial intelligence technologies. I mainly study efficient methods for processing high-dimensional data based on low-rank models. My work is dedicated to addressing prevalent issues in the big data industry, such as complex data structures, strong noise, damaged or missing data, thereby overcoming the limitations of traditional machine learning methods. Throughout years of research, I have employed non-convex optimization methods based on low-rank three-dimensional arrays, successfully tackling fundamental challenges such as multi-view data subspace clustering, high-dimensional data completion, three-dimensional image salient region detection, and video foreground-background separation. My research achievements have made a significant impact both domestically and internationally, receiving high praise from peers in the field. Pertinent research results have been published in authoritative international journals such as IEEE T-SP (1 as the first author), SIAM Journal on Imaging Sciences (1 as the first author), and Pattern Recognition (1 as the first author and 1 as the corresponding author), covering areas like pattern recognition and machine learning. Additionally, I have collaborated with several co-authors to publish over 10 papers in JCR Q1 journals, including Neural Networks, Information Sciences, IEEE Transactions on Cybernetics (T-CYB), T-MM, T-KDE, T-IP, among others. These works have been cited nearly 200 times on Google Scholar. My research has been positively recognized and cited by scholars from countries including China, the United States, and Belgium.
教育经历
2016 年-- 2023 年，博士，计算机， 南伊利诺伊大学计算机系，卡本代尔， 美国伊利诺伊州，
2007 年--2012 年，博士，理论和应用数学，德克萨斯农工大学数学系， 学院站，美国德克萨斯州，
2003 年-- 2007 年，学士，理论和应用数学， 吉林大学数学系，中国长春
Ph.D., Computer Science, Department of Computer Science, Southern Illinois University, Carbondale, IL, 2016 – 2023
Ph.D., Pure and Applied Mathematics, Department of Mathematics, Texas A&M University, College Station, Texas, 2007 - 2012
B.S., Pure and Applied Mathematics, Department of Mathematics, Jilin University, Changchun, China, 2003 - 2007
工作经历
2019年秋 --2022年春 ，马萨诸塞州韦斯特菲尔德州立大学数学系和计算机与信息科学系 ——数据科学助理教授
Assistant Professor of Data Science Department of Mathematics & Department of Computer and Information Science, Westfield State University, MA, 2019 - 2022
近期论文
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代表作：
1. Yang, Ming, Qilun Luo, Wen Li, and Mingqing Xiao. "3D Array Image Data Completion by Tensor Decomposition and Nonconvex Regularization Approach." IEEE Transactions on Signal Processing, vol. 70, pp. 4291-4304, 2022.
2. Yang, Ming, Qilun Luo, Wen Li, and Mingqing Xiao. "Multiview clustering of images with tensor rank minimization via nonconvex approach." SIAM Journal on Imaging Sciences 13, no. 4 (2020): 2361-2392.
3. Yang Ming, Qilun Luo, Wen Li, and Mingqing Xiao. "Nonconvex 3D array image data recovery and pattern recognition under tensor framework." Pattern Recognition (Elsevier) 122 (2022): 108311.
4. Yang, Ming, Wen Li, and Mingqing Xiao. "On identifiability of higher order block term tensor decompositions of rank Lr⊗ rank-1." Linear and Multilinear Algebra (Taylor & Francis) 68, no. 2 (2020): 223-245.
5. Quan Yu and Ming Yang* (通讯)"Low Rank Tensor Recovery via Non-convex Regularization, Structured
Factorization and Spatio-Temporal Characteristics” Pattern Recognition (Elsevier) (2023): 109343
其他：
1. Tingquan Deng; Jingyu Wang; Qingwei Jia; Ming Yang, Semi-supervised sparse representation collaborative clustering of incomplete data. Appl Intell 53, 31077–31105 (2023)
2. Zhou, Qian, Qianqian Wang, Quanxue Gao, Ming Yang, and Xinbo Gao. "Unsupervised Discriminative Feature Selection via Contrastive Graph Learning." IEEE Transactions on Image Processing (2024).
3. Li, Jing, Quanxue Gao, Qianqian Wang, Ming Yang, and Wei Xia, Orthogonal Non-negative Tensor Factorization based Multi-view Clustering. In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). 2023.
4. Li, Jing, Quanxue Gao, Qianqian Wang, Ming Yang, and Xinbo Gao, Efficient Anchor Graph Factorization for Multi-view Clustering. IEEE Transactions on Multimedia. 2023.
5. Lu, Han, Huafu Xu, Qianqian Wang, Quanxue Gao, Ming Yang and Xinbo Gao, Efficient Multi-View K-Means for Image Clustering. IEEE Transactions on Image Processing, 2023.
6. Luo, Qilun, Ming Yang, Wen Li, and Mingqing Xiao. "Hyper-Laplacian Regularized Multi-View Clustering with Exclusive L21 Regularization and Tensor Log-Determinant Minimization Approach." ACM Transactions on Intelligent Systems and Technology 14, no. 3 (2023): 1-29.
7. Deng, Tingquan, Ge Yang, Yang Huang, Ming Yang, and Hamido Fujita. "Adaptive multi-granularity sparse subspace clustering." Information Sciences 642 (2023): 119143.
8. Qilun Luo, Ming Yang, Wen Li, and Mingqing Xiao. "Multi-Dimensional Data Processing with Bayesian Inference via Structural Block Decomposition." IEEE Transactions on Cybernetics (2023).
9. Xie, Deyan, Quanxue Gao, and Ming Yang. "Enhanced tensor low-rank representation learning for multi-view clustering." Neural Networks 161 (2023): 93-104.
10. Mei, Shikun, Wenhui Zhao, Quanxue Gao, Ming Yang, and Xinbo Gao. "Joint feature selection and optimal bipartite graph learning for subspace clustering." Neural Networks 164 (2023): 408-418.
11. Yun, Yu, Jing Li, Quanxue Gao, Ming Yang, and Xinbo Gao. "Low-rank discrete multi-view spectral clustering." Neural Networks 166 (2023): 137-147.
12. Zhou, Qian, Quanxue Gao, Qianqian Wang, Ming Yang, and Xinbo Gao. "Sparse discriminant PCA based on contrastive learning and class-specificity distribution." Neural Networks 167 (2023): 775-786.
13. Li, Guangfei, Quanxue Gao, Ming Yang, and Xinbo Gao. "Active learning based on similarity level histogram and adaptive-scale sampling for very high resolution image classification." Neural Networks 167 (2023): 22-35.
14. Zhao, Wenhui, Quanxue Gao, Shikun Mei, and Ming Yang. "Contrastive self-representation learning for data clustering." Neural Networks 167 (2023): 648-655.
15. Xia, Wei, Tianxiu Wang, Quanxue Gao, Ming Yang, and Xinbo Gao. "Graph embedding contrastive multi-modal representation learning for clustering." IEEE Transactions on Image Processing 32 (2023): 1170-1183.
16. Xia, Wei, Quanxue Gao, Xinbo Gao, Ming Yang. " Self-consistent Contrastive Attributed Graph Clustering with Pseudo-label Prompt” IEEE Transactions on Multimedia (2022).
17. Shu, Xiaochuang, Quanxue Gao, Wei Xia, Ming Yang, and Xinbo Gao. "Self-weighted anchor graph learning for multi-view clustering.” IEEE Transactions on Multimedia (2022).
18. Sun, Xiaoli, Youjuan Wang, Ming Yang, and Xiujun Zhang. "Sliced sparse gradient induced multi-view subspace clustering via tensorial arctangent rank minimization." IEEE Transactions on Knowledge and Data Engineering. 2022 Jun 21.
19. Lv, Ziyu, Quanxue Gao, Xiangdong Zhang, Qin Li, and Ming Yang "View-consistency learning for incomplete multi-view clustering.” IEEE Transactions on Image Processing, vol. 31, pp. 4790-4802, 2022
20. Yang, Haizhou, Quanxue Gao, Wei Xia, Ming Yang, and Xinbo Gao. "Multi-view Spectral Clustering with Bipartite Graph." IEEE Transactions on Image Processing, vol. 31, pp. 3591-3605, 2022
21. Li, Qin, Mingzhen Hou, Hong Lai, Ming Yang. "Cross-modal Distribution Alignment Embedding Network for Generalized Zero-shot Learning." Neural Networks (Elsevier) 146 (2022)
22. Xia, Wei, Sen Wang, Ming Yang, Quanxue Gao, Jungong Han, and Xinbo Gao. "Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation." Neural Networks (Elsevier) 145 (2022): 1-9.
23. Cai, Shuting, Qilun Luo, Ming Yang*, Wen Li, and Mingqing Xiao*. "Tensor robust principal component analysis via non-convex low rank approximation." Applied Sciences (MDPI) 9, no. 7 (2019).
24. Cai, Shuting, Kun Liu, Ming Yang*, Jianliang Tang, Xiaoming Xiong, and Mingqing Xiao. "A new development of non-local image denoising using fixed-point iteration for non-convex ？p sparse optimization." PloS one 13, no. 12 (2018): e0208503.
25. Peng, Chong, Zhao Kang, Ming Yang, and Qiang Cheng. "Feature selection embedded subspace clustering." IEEE Signal Processing Letters 23, no. 7 (2016): 1018-1022.
26. Yang, Ming. "On partial and generic uniqueness of block term tensor decompositions." Annali dell' Università di Ferrara (Springer) 60, no. 2 (2014): 465-493.
27. Foias, Ciprian, M. S. Jolly, and Ming Yang. "On single mode forcing of the 2D-NSE." Journal of Dynamics and Differential Equations (Springer) 25, no. 2 (2013): 393-433.
28. Lu, Han, Quanxue Gao, Qianqian Wang, Ming Yang, and Wei Xia. "Centerless multi-view K-means based on the adjacency matrix." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 7, pp. 8949-8956. 2023.
29. Peng, Chong, Zhao Kang, Ming Yang, and Qiang Cheng. "RAP: Scalable RPCA for low-rank matrix recovery." In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 2113-2118. 2016.
30. Kang, Zhao, Chong Peng, Ming Yang, and Qiang Cheng. "Top-n recommendation on graphs." In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 2101-2106. 2016.
31. Kang, Zhao, Chong Peng, Ming Yang, and Qiang Cheng. "Exploiting nonlinear relationships for top-n recommender systems." In 2017 IEEE International Conference on Big Knowledge (ICBK), pp. 49-56. IEEE, 2017.
32. Yang, Ming, Dunren Che, Wen Liu, Zhao Kang, Chong Peng, Mingqing Xiao, and Qiang Cheng. "On identifiability of 3-tensors of multilinear rank (1,Lr,Lr) ." Big Data and Information Analytics (BDIA), American Institute of Mathematical Sciences, Vol. 1, no. 4, October 2016.
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