Multi-Class ASD Classification via Label Distribution Learning with Class-Shared and Class-Specific Decomposition.

Abstract

The behavioral and cognitive deficits in autism spectrum disorder (ASD) patients are associated with abnormal brain function. The resting-state functional magnetic resonance imaging (rs-fMRI) is an effective non-invasive tool for revealing the brain dysfunction for ASD patients. However, most rs-fMRI based ASD diagnosis methods are developed for simple binary classification, instead of classification of multiple sub-types in ASD. Besides, they assume that the class boundary in ASD classification is crisp, whereas the symptoms of ASD sub-types are a continuum from mild to severe impairments in both social communication and restrictive repetitive behaviors/interests, and do not have crisp boundary between each other. To this end, we introduce label distribution learning (LDL) into multi-class ASD classification and propose LDL-CSCS under the LDL framework. Specifically, the label distribution is introduced to describe how individual disease labels correlate with the subject. In the learning crierion of LDL-CSCS, the label distribution is decomposed into the class-shared and class-specific components, in which the class-shared component records the common knowledge across all persons and the class-specific component records the specific information in each ASD sub-type. Low-rank constraint is imposed on the class-shared component whereas the group sparse constraint is imposed on the class-specific component, respectively. An Augmented Lagrange Method (ALM) is developed to find the optimal solution. The experimental results show that the proposed method for ASD diagnosis has superior classification performance, compared with some existing algorithms.