Label-Alignment-Based Multi-Task Feature Selection for Multimodal Classification of Brain Disease

Abstract

Recently, multi-task feature selection methods have been applied to jointly identify the disease-related brain regions for fusing information from multiple modalities of neuroimaging data. However, most of those approaches ignore the complementary label information across modalities. To address this issue, in this paper, we present a novel label-alignment-based multi-task feature selection method to jointly select the most discriminative features from multi-modality data. Specifically, the feature selection procedure of each modality is treated as a task and a group sparsity regularizer (i.e., $\ell _{2,1}$ norm) is adopted to ensure that only a small number of features to be selected jointly. In addition, we introduce a new regularization term to preserve label relatedness. The function of the proposed regularization term is to align paired within-class subjects from multiple modalities, i.e., to minimize their distance in corresponding low-dimensional feature space. The experimental results on the magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed method can achieve better performances over state-of-the-art methods on multimodal classification of Alzheimer’s disease (AD) and mild cognitive impairment (MCI).