Journal of Frontiers of Computer Science and Technology ›› 2020, Vol. 14 ›› Issue (12): 2083-2093.DOI: 10.3778/j.issn.1673-9418.1908067

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Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis

ZHANG Chunxiang, WANG Jun, ZHANG Jiaxu, DENG Zhaohong, PAN Xiang, WANG Shitong   

  1. 1. School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China
    2. School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China
  • Online:2020-12-01 Published:2020-12-11



  1. 1. 江南大学 数字媒体学院,江苏 无锡 214122
    2. 上海大学 通信与信息工程学院,上海 200444


Autism is a neurodevelopmental disorder with great uncertainty in its diagnosis. However, the existing modeling methods for autism diagnosis have not been effectively studied for the uncertainty of the diagnosis process so far. In this paper, based on TSK (Takagi-Sugeno-Kang) fuzzy system and combining the association information between functional connections, a new sparse modeling method JGSL-TSK (joint-group-sparse-learning Takagi-Sugeno-Kang) for uncertain joint group is proposed and applied to the auxiliary diagnosis of autism. Firstly, the original rs-fMRI (resting-state functional magnetic resonance imaging) data are preprocessed and extracted to obtain the reduced dimension feature matrix. Secondly, based on the TSK fuzzy system framework, the joint sparse regulari-zation term is introduced to the consequent parameter learning process from the correlation between features, so as to guide the joint selection of features within the same rule and between rules. Finally, the alternating optimization method is used to solve the model. Compared with the existing methods, this method has the advantages of strong interpretability and good classification performance. Experimental results show that this method is conducive to the auxiliary diagnosis of autism.

Key words: autism spectrum disorder (ASD), resting-state functional magnetic resonance imaging (rs-fMRI), TSK fuzzy system, joint group sparse



关键词: 自闭症谱系障碍, 静息态功能磁共振成像, TSK模糊系统, 联合组稀疏