Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 474-478.doi: 10.11896/jsjkx.200100037

• Big Data & Data Science • Previous Articles     Next Articles

Feature Selection Method Combined with Multi-manifold Structures and Self-representation

YI Yu-gen1, LI Shi-cheng1, PEI Yang1, CHEN Lei1, DAI Jiang-yan2   

  1. 1 School of Software,Jiangxi Normal University,Nanchang 330022,China
    2 School of Computer Engineering,Weifang University,Weifang,Shandong 261061,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:YI Yu-gen,born in 1986,Ph.D,lecturer.His research interests include artificial intelligence,computer vision,and machine learning.
    DAI Jiang-yan,born in 1985,postdoctor,associate professor.Her main research interests include image inpain-ting and computer vision.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61602221,61806126),Natural Science Foundation of Jiangxi Province (20171BAB212009),Science and Technology Research Project of Jiangxi Provincial Department of Education (GJJ160315,GJJ170234) and Youth Innovation Science and Technology Support Plan of Shandong Provincial Institution of Higher Education (2019KJN012).

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Abstract: Feature selection is to reduce the dimension of data by removing irrelevant and redundant features and improve the efficiency of learning algorithm.Unsupervised feature selection has become one of the challenging problems in dimensionality reduction.Firstly,combining self-representation and manifold structure of features,a Joint Multi-Manifold Structures and Self-Representation (JMMSSR) unsupervised feature selection algorithm is proposed.Different from the existing approaches,our approach designs an adaptive weighted strategy to integrate multi-manifold structures to describe the structure of features accurately.Then,a simple and effective iterative updating algorithm is proposed to solve the objective function,and the convergence of the optimization algorithm is also verified by numerical experiments.Finally,experimental results on three datasets (such as JAEEF,ORL and COIL20) show that the proposed approach exhibits better performance than the existing unsupervised feature selection approaches.

Key words: Adaptive weighted, Feature selection, Multi-manifold structures, Self-representation

CLC Number: 

  • TP391
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