Feature Selection Method for Neuropsychiatric Disorder Based on Adaptive Sparse Structure Learning
HAO Shijie1,2, GUO Yanrong1,2, CHEN Tao1,2, WANG Meng1,2, HONG Richang1,2
1. Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Hefei 230601 2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601
Abstract：In the research of computer-aided diagnosis techniques for neuropsychiatric diseases, professionals are required to perform diagnostic-level semantic annotations on samples, and it is time-consuming and labor-intensive. Therefore, it is of great importance to develop unsupervised techniques for the computer-aided diagnosis on neuropsychiatric diseases. In this paper, an unsupervised feature selection method based on adaptive sparse structure learning is proposed and applied to the task of diagnosis on Schizophrenia and Alzheimer′s disease. The sparse representation and the data manifold structure are simultaneously learned in a unified framework. In this framework, the generalized norm is adopted to model the reconstruction error of sparse learning. The manifold structure of the whole dataset is iteratively updated. The lacking of robustness in the traditional feature selection methods is relieved. Experiments on two public datasets of Schizophrenia and Alzheimer′s disease demonstrate the effectiveness of the proposed method in classification of neuropsychiatric diseases.
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2021, Vol. 34 
