计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 220900003-9.doi: 10.11896/jsjkx.220900003

• 大数据&数据科学 • 上一篇    下一篇

基于图嵌入的正交局部保持投影无监督特征选择

朱建勇1,2, 李兆祥1,2, 徐彬1,2, 杨辉1,2, 聂飞平3   

  1. 1 华东交通大学电气与自动化工程学院 南昌 330013
    2 江西省先进控制与优化重点实验室(华东交通大学) 南昌 330013
    3 西北工业大学光电与智能研究院 西安 710072
  • 发布日期:2023-11-09
  • 通讯作者: 聂飞平(feipingnie@gmail.com)
  • 作者简介:(zhujyemail@163.com)
  • 基金资助:
    国家自然科学基金(61963015,61733005)

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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摘要： 传统的基于图学习的无监督特征选择算法通常采用稀疏正则化方法来选择特征,但这种方法过于依赖于图学习的效率,并且存在正则化参数调优困难等问题。为解决这些问题,针对性地提出了一种基于图嵌入学习的正交局部保持投影无监督特征选择(Orthogonal Locality Preserving Projection Unsupervised Feature Selection via Graph Embedding,OLPPFS) 算法。首先,利用能够保持数据局部几何流形结构的局部保持投影方法增强数据的线性映射能力,同时约束正交方向投影以方便数据重构;其次,通过图嵌入学习方法快速构建稀疏相似图来描述样本数据的内在结构;接着,采用$\ell$2,0范数约束投影矩阵的值,准确选择指定数目的判别性特征子集;最后,针对$\ell$2,0范数NP难题,设计一种有效求解$\ell$2,0范数问题的无参迭代算法求解该模型。仿真结果表明了所提算法的有效性和优越性。

关键词: 无监督特征选择, 正交局部保持投影, 图嵌入学习, $\ell$2,0范数, 无参迭代算法

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

中图分类号: 

  • TP391
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