关键词: 超图学习, 多任务学习, 特征选择, 多模态分类, 阿尔茨海默症

Abstract:  Machine learning has been widely applied to the diagnosis of brain diseases at present. The application of medical imaging analysis with limited samples and large feature dimension is a typical small sample problem. In addition, multi-modal data (such as MRI and PET) can be obtained via different imaging procedures. This paper proposes a novel feature selection method to handle the small sample of multi-modal imaging data. Firstly, each modality is treated as a single learning task and the l_2,1 norm is used to jointly select features, which can guarantee the features of different modalities in the same brain regions to be selected at the same time. And hypergraph learning is involved to capture the high-order relationships among the samples, which can depict the priori distribution of data in each modality. Then, the multi-kernel support vector machine (SVM) is used to fuse the features chosen from multi-modal data for final classification. The proposed method is validated and compared with traditional algorithms on ADNI dataset, and the experimental results show the efficiency of the proposed method.

Key words: hypergraph learning, multi-task learning, feature selection, multi-modal classification, Alzheimer’s disease