Multi-level multi-task structured sparse learning for diagnosis of schizophrenia disease

Abstract

In recent studies, it has attracted increasing attention in multi-frequency bands analysis for diagnosis of schizophrenia (SZ). However, most existing feature selection methods designed for multi-frequency bands analysis do not take into account the inherent structures (i.e., both frequency specificity and complementary information) from multi-frequency bands in the model, which are limited to identify the discriminative feature subset in a single step. To address this problem, we propose a multi-level multi-task structured sparse learning (MLMT-TS) framework to explicitly consider the common features with a hierarchical structure. Specifically, we introduce two regularization terms in the hierarchical framework to impose the common features across different bands and the specificity from individuals. Then, the selected features are used to construct multiple support vector machine (SVM) classifiers. Finally, we adopt an ensemble strategy to combine outputs of all SVM classifiers to achieve the final decision. Our method has been evaluated on 46 subjects, and the superior classification results demonstrate the effectiveness of our proposed method as compared to other methods.