Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease

Multi-modality based classification methods are superior to the single modality based approaches for the automatic diagnosis of the Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most of the multi-modality based methods usually ignore the structure information of data and simply squeeze them to pairwise relationships. In real-world applications, the relationships among subjects are much more complex than pairwise, and the high-order structure containing more discriminative information will be intuitively beneficial to our learning tasks. In light of this, a hypergraph based multi-task feature selection method for AD/MCI classification is proposed in this paper. Specifically, we first perform feature selection on each modality as a single task and incorporate group-sparsity regularizer to jointly select common features across multiple modalities. Then, we introduce a hypergraph based regularization term for the standard multi-task feature selection to model the high-order structure relationship among subjects. Finally, a multi-kernel support vector machine is adopted to fuse the features selected from different modalities for the final classification. The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed method achieves better classification performance than the start-of-art multi-modality based methods. (C) 2019 Elsevier Ltd. All rights reserved.

基于多模态的分类方法在阿尔茨海默病（AD）和轻度认知障碍（MCI）的自动诊断方面优于基于单模态的方法。然而，大多数基于多模态的方法通常忽略数据的结构信息，只是将其压缩为成对关系。在实际应用中，研究对象之间的关系比成对关系要复杂得多，包含更多判别信息的高阶结构直观上对我们的学习任务有益。有鉴于此，本文提出了一种基于超图的用于AD/MCI分类的多任务特征选择方法。具体而言，我们首先将每个模态作为单个任务进行特征选择，并结合组稀疏正则化器来联合选择跨多个模态的共同特征。然后，我们为标准的多任务特征选择引入一个基于超图的正则化项，以对研究对象之间的高阶结构关系进行建模。最后，采用多核支持向量机融合从不同模态中选择的特征进行最终分类。在阿尔茨海默病神经影像学倡议（ADNI）上的实验结果表明，我们提出的方法比现有的基于多模态的先进方法取得了更好的分类性能。（C）2019爱思唯尔有限公司。保留所有权利。