Diagnosis of Alzheimer's disease using hypergraph p-Laplacian regularized multi-task feature learning.

Multimodal data-based classification methods have been widely used in the diagnosis of Alzheimer's disease (AD) and have achieved better performance than single-modal-based methods. However, most classification methods based on multimodal data tend to consider only the correlation between different modal data and ignore the inherent non-linear higher-order relationships between similar data, which can improve the robustness of the model. Therefore, this study proposes a hypergraph p-Laplacian regularized multi-task feature selection (HpMTFS) method for AD classification. Specifically, feature selection for each modal data is considered as a distinct task and the common features of multimodal data are extracted jointly by group-sparsity regularizer. In particular, two regularization terms are introduced in this study, namely (1) a hypergraph p-Laplacian regularization term to retain higher-order structural information for similar data, and (2) a Frobenius norm regularization term to improve the noise immunity of the model. Finally, using a multi-kernel support vector machine to fuse multimodal features and perform the final classification. We used baseline sMRI, FDG-PET, and AV-45 PET imaging data from 528 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate our approach. Experimental results show that our HpMTFS method outperforms existing multimodal-based classification methods.