Feature Selection Method for Neuropsychiatric Disorder Based on Adaptive Sparse Structure Learning

被引：0

作者：

Hao S. ^{[1
,2
]}

Guo Y. ^{[1
,2
]}

Chen T. ^{[1
,2
]}

Wang M. ^{[1
,2
]}

Hong R. ^{[1
,2
]}

机构：

[1] Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Hefei

[2] School of Computer Science and Information Engineering, Hefei University of Technology, Hefei

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 04期

基金：

安徽省自然科学基金; 国家重点研发计划; 中国国家自然科学基金;

关键词：

Adaptive sparse structure learning; Manifold learning; Neuropsychiatric disorder study; Unsupervised feature selection;

D O I：

10.16451/j.cnki.issn1003-6059.202104003

中图分类号：

学科分类号：

摘要：

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. © 2021, Science Press. All right reserved.

引用

页码：311 / 321

页数：10

共 29 条

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