Multi-label feature selection based on dynamic graph Laplacian

Yonghao LI; Liang HU; Ping ZHANG; Wanfu GAO

doi:10.11959/j.issn.1000-436X.2020244

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Multi-label feature selection based on dynamic graph Laplacian

Papers | 更新时间：2024-06-05

- Multi-label feature selection based on dynamic graph Laplacian
- Journal on Communications Vol. 41, Issue 12, Pages: 47-59(2020)
- 作者机构：
  
  1. 吉林大学计算机科学与技术学院，吉林长春130012
  2. 吉林大学符号计算与知识工程教育部重点实验室，吉林长春 130012
  3. 吉林大学化学学院，吉林长春130012
- 作者简介：
- 基金信息：
  
  Postdoctoral Innovative Talents Support Program under Grant(BX20190137);China Postdoctoral Science Foundation Founded Project(2020M670839);The National Key Research and Development Program of China(2017YFA0604500);Key Scientific and Technological Research and Development Plan of Jilin Province(20180201103GX)
- DOI：10.11959/j.issn.1000-436X.2020244
  CLC： TP18
- Online First：2020-12，
  
  Published：25 December 2020
- 稿件说明：
移动端阅览
Yonghao LI, Liang HU, Ping ZHANG, et al. Multi-label feature selection based on dynamic graph Laplacian[J]. Journal on Communications, 2020, 41(12): 47-59.
DOI：

Yonghao LI, Liang HU, Ping ZHANG, et al. Multi-label feature selection based on dynamic graph Laplacian[J]. Journal on Communications, 2020, 41(12): 47-59. DOI： 10.11959/j.issn.1000-436X.2020244.

摘要

针对基于图的多标签特征选择方法忽略图拉普拉斯矩阵的动态变化，且利用逻辑标签来指导特征选择过程而丢失标签信息等问题，提出了一种基于动态图拉普拉斯矩阵和实值标签的多标签特征选择方法。该方法利用特征矩阵的稳健低维空间构造动态图拉普拉斯矩阵，并利用该稳健低维空间作为实值标签空间，进一步使用流形约束和非负约束将逻辑标签转化为实值标签，以此来解决上述问题。所提方法与3种多标签特征选择方法在9个多标签基准数据集上进行了对比实验，实验结果表明，所提多标签特征选择方法可得到高质量的特征子集，并且能获得很好的分类表现。

Abstract

In view of the problems that graph-based multi-label feature selection methods ignore the dynamic change of graph Laplacian matrix

as well as such methods employ logical-value labels to guide feature selection process and loses label information

a multi-label feature selection method based on both dynamic graph Laplacian matrix and real-value labels was proposed.The robust low-dimensional space of feature matrix was used to construct a dynamic graph Laplacian matrix

and the robust low-dimensional space was used as the real-value label space.Furthermore

manifold and non-negative constraints were adopted to transform logical labels into real-valued labels to address the issues mentioned above.The proposed method was compared to three multi-label feature selection methods on nine multi-label benchmark data sets in experiments.The experimental results demonstrate that the proposed multi-label feature selection method can obtain the higher quality feature subset and achieve good classification performance.

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references

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