Research on Feature Selection Method of Group Lasso Hypergraph Regularization and Depression Classification

Abstract

Purposes In the research of depression classification and diagnosis, feature selection plays a crucial role. Methods To address the issues of missing group effect information in existing hypergraph regularized feature selection methods, the group lasso-based hypergraph regularized feature selection approach is proposed. Specifically, the functional magnetic resonance imaging (fMRI) dataset is preprocessed first for depression. Second, on the basis of the preprocessed fMRI data, five brain network models under different scales are constructed and the topological attributes are calculated to extract features. After feature extracting, the group lasso method is introduced to build hypergraph and the hypergraph regularized feature selection method is employed to select features. At last, classification model is constructed by using support vector machine (SVM) and its performance is evaluated. Additionally, the effectiveness of the proposed method is validated on UCI datasets. Findings The demonstrate that the proposed method outperforms traditional feature selection methods across five different node templates. Moreover, for similar numbers of nodes in different templates, superior classification diagnostic performance is achieved.

Keywords

• hypergraph
• feature selection
• group lasso
• sparse
• classification
• depression