Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 220900003-9.doi: 10.11896/jsjkx.220900003

• Big Data & Data Science • Previous Articles     Next Articles

Orthogonal Locality Preserving Projection Unsupervised Feature Selection Based on Graph Embedding

ZHU Jianyong1,2, LI Zhaoxiang1,2, XU Bin1,2, YANG Hui1,2, NIE Feiping3   

  1. 1 School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China
    2 Key Laboratory of Advanced Control & Optimization of Jiangxi Province(East China Jiaotong University),Nanchang 330013,China
    3 School of Artificial Intelligence,OPtics and ElectroNics(iOPEN),Northwestern Polytechnical University,Xi'an 710072,China
  • Published:2023-11-09
  • About author:ZHU Jianyong,born in 1977,Ph.D,associate professor,master supervisor.His main research interests include data analysis,random distribution control and predictive control.
    NIE Feiping,born in 1977,Ph.D,professor,Ph.D supervisor.His main research interests include machine learning and its applications,such as pattern recognition,data mining,computer vision,image processing and information retrieval.
  • Supported by:
    National Natural Science Foundation of China(61963015,61733005).

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Abstract: The traditional unsupervised feature selection algorithm based on graph learning often adopts sparse regularization method.However,this approach relies too heavily on the efficiency of graph learning,and it is not easy to tune regularization parameters.To solve this problem,an unsupervised feature selection algorithm based on graph embedding learning with orthogonal locality preserving projection is proposed in this paper.Firstly,we utilize locality preserving projection method to enhance the linear mapping ability that can maintain the local geometric manifold structure of the data,and orthogonal projection mode brings convenience to data reconstruction.Moreover,we use graph embedding learning method to quickly learn the similarity matrix of data.Then,$\ell$2,0-norm constrained projection matrix to select discriminative features.Finally,a new nonparametric algorithm is used to efficiently solve the model problem iteratively since $\ell$2,0-norm belongs to NP problem.Experimental results prove the effectiveness and superiority of the proposed algorithm.

Key words: Unsupervised feature selection, Orthogonal locality preserving projection, Graph embedding learning, $\ell$2, 0-norm, Nonparametric iterative algorithm

CLC Number: 

  • TP391
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