联合图嵌入与特征加权的无监督特征选择

张巍; 张圳彬

doi:10.12052/gdutxb.210053

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联合图嵌入与特征加权的无监督特征选择

更新时间：2026-05-27

- 联合图嵌入与特征加权的无监督特征选择
- Joint Graph Embedding and Feature Weighting for Unsupervised Feature Selection
- 广东工业大学学报 2021年38卷第5期页码：16-23
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  张巍(1964–)，女，副教授，主要研究方向为网络安全、数据挖掘、协同计算和模式识别
- 基金信息：
  
  广东省重点领域研发计划项目(2020B010166006);国家自然科学基金资助项目(61972102);广东省教育厅资助项目(粤教高函〔2018〕 179号，粤教高函〔2018〕 1号);广州市科技计划项目(201903010107，201802030011，201802010026，201802010042，201604046017)
- DOI：10.12052/gdutxb.210053
  中图分类号： TP391
- 网络首发：2021-09-10，
  
  纸质出版：2021
- 稿件说明：
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张巍, 张圳彬. 联合图嵌入与特征加权的无监督特征选择[J]. 广东工业大学学报, 2021, 38(5): 16-23.

Zhang Wei, Zhang Zhen-bin. Joint Graph Embedding and Feature Weighting for Unsupervised Feature Selection[J]. 2021, 38(5): 16-23.
张巍, 张圳彬. 联合图嵌入与特征加权的无监督特征选择[J]. 广东工业大学学报, 2021, 38(5): 16-23. DOI： 10.12052/gdutxb.210053.

Zhang Wei, Zhang Zhen-bin. Joint Graph Embedding and Feature Weighting for Unsupervised Feature Selection[J]. 2021, 38(5): 16-23. DOI： 10.12052/gdutxb.210053.

摘要

在特征选择领域

现有的大多数方法不能同时捕获不同特征有差异的权重

不能对投影子空间施加正交约束来提高特征的判别力。为此

本文提出联合图嵌入与特征加权的无监督特征选择方法(Joint Graph Embedding and Feature Weighting

JGEFW)。首先

通过图嵌入局部结构学习获得相似度矩阵和聚类指示矩阵; 然后利用正交回归获得表征不同特征重要程度的权重矩阵

以此选择出判别力强且非冗余的特征。此外

本文还提出了一个交替迭代优化算法来求解JGEFW模型; 最后

在4个数据集上进行实验验证。实验结果表明

JGEFW的聚类指标在大多数情况下优于其他对比算法。

Abstract

In the area of feature selection

most existing methods cannot simultaneously capture the importance of different features and enforce orthogonal constraint on the projected subspace to improve the discrimination of selected features. To deal with this issue

a new unsupervised feature selection method called joint graph embedding and feature weighting (JGEFW) is proposed. To be specific

graph-embedding local structure learning is leveraged to obtain the similarity matrix and the cluster indicator matrix. Moreover

the weight matrix is learned through the orthogonal regression

to select the discriminative and non-redundant features. At last

an alternating iteration algorithm is developed for solving the proposed objective function. Finally

experimental results about clustering evaluation metrics on four publicly available datasets demonstrate that JGEFW is better than others in most cases.

关键词

Keywords

references

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