Identification of Alzheimer's disease (AD) with multimodal neuroimaging data has been receiving increasing attention. However, the presence of numerous redundant features and corrupted neuroimages within multimodal datasets poses significant challenges for existing methods. In this paper, we propose a feature selection method named Enhanced Multimodal Low-rank Embedding (EMLE) for multimodal AD diagnosis. Unlike previous methods utilizing convex relaxations of the ℓ2,0-norm, EMLE exploits an ℓ2,γ-norm regularized projection matrix to obtain an embedding representation and select informative features jointly for each modality. The ℓ2,γ-norm, employing an upper-bounded nonconvex Minimax Concave Penalty (MCP) function to characterize sparsity, offers a superior approximation for the ℓ2,0-norm compared to other convex relaxations. Next, a similarity graph is learned based on the self-expressiveness property to increase the robustness to corrupted data. As the approximation coefficient vectors of samples from the same class should be highly correlated, an MCP function introduced norm, i.e., matrix γ-norm, is applied to constrain the rank of the graph. Furthermore, recognizing that diverse modalities should share an underlying structure related to AD, we establish a consensus graph for all modalities to unveil intrinsic structures across multiple modalities. Finally, we fuse the embedding representations of all modalities into the label space to incorporate supervisory information. The results of extensive experiments on the Alzheimer's Disease Neuroimaging Initiative datasets verify the discriminability of the features selected by EMLE.

中文翻译：

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用于多模态阿尔茨海默病诊断的增强型多模态低秩嵌入特征选择模型


利用多模态神经影像数据识别阿尔茨海默病（AD）已受到越来越多的关注。然而，多模态数据集中存在大量冗余特征和损坏的神经图像，对现有方法提出了重大挑战。在本文中，我们提出了一种用于多模态 AD 诊断的特征选择方法，称为增强型多模态低秩嵌入（EMLE）。与之前利用 ℓ2,0-范数的凸松弛的方法不同，EMLE 利用 ℓ2,γ-范数正则化投影矩阵来获得嵌入表示并为每种模态联合选择信息特征。 ℓ2,γ-范数采用上界非凸极小最大凹罚分 (MCP) 函数来表征稀疏性，与其他凸松弛相比，为 ℓ2,0-范数提供了更好的近似。接下来，基于自我表达特性学习相似图，以提高对损坏数据的鲁棒性。由于同一类样本的逼近系数向量应该高度相关，因此采用引入范数的MCP函数，即矩阵γ-范数来约束图的秩。此外，认识到不同的模式应该共享与 AD 相关的底层结构，我们为所有模式建立了一个共识图，以揭示跨多种模式的内在结构。最后，我们将所有模态的嵌入表示融合到标签空间中以纳入监督信息。对阿尔茨海默氏病神经影像计划数据集进行的大量实验结果验证了 EMLE 所选特征的可区分性。