联合低秩表示与图嵌入的无监督特征选择

滕少华; 冯镇业; 滕璐瑶; 房小兆

doi:10.12052/gdutxb.190048

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联合低秩表示与图嵌入的无监督特征选择

综合研究 | 更新时间：2026-05-27

- 联合低秩表示与图嵌入的无监督特征选择
- Joint Low-Rank Representation and Graph Embedding for Unsupervised Feature Selection
- 广东工业大学学报 2019年36卷第5期页码：7-13
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 维多利亚大学应用信息中心, 维多利亚州墨尔本 VIC,3011
- 作者简介：
  
  滕少华(1962−), 男, 教授, 博士，主要研究方向为大数据、角色分配、网络安全、协同计算、机器学习
- 基金信息：
  
  国家自然科学基金资助项目(61702110, 61772141);广东省教育厅项目(粤教高函〔2018〕179号);广州市科技计划项目(201802010042)
- DOI：10.12052/gdutxb.190048
  中图分类号： TP391.4
- 网络首发：2019-08-21，
  
  纸质出版：2019
- 稿件说明：
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滕少华, 冯镇业, 滕璐瑶, 房小兆. 联合低秩表示与图嵌入的无监督特征选择[J]. 广东工业大学学报, 2019, 36(5): 7-13.

Teng Shao-hua, Feng Zhen-ye, Teng Lu-yao, et al. Joint Low-Rank Representation and Graph Embedding for Unsupervised Feature Selection[J]. 2019, 36(5): 7-13.
滕少华, 冯镇业, 滕璐瑶, 房小兆. 联合低秩表示与图嵌入的无监督特征选择[J]. 广东工业大学学报, 2019, 36(5): 7-13. DOI： 10.12052/gdutxb.190048.

Teng Shao-hua, Feng Zhen-ye, Teng Lu-yao, et al. Joint Low-Rank Representation and Graph Embedding for Unsupervised Feature Selection[J]. 2019, 36(5): 7-13. DOI： 10.12052/gdutxb.190048.

摘要

大数据应用带来高维数据急剧增加

数据降维已成为重要问题. 特征选择降维方法已广泛应用于模式识别领域

近年来提出了许多基于流形学习的特征选择方法

然而这类方法往往容易受到各种噪声影响. 对此

本文提出一种联合低秩表示和图嵌入的高效无监督特征选择方法(JLRRGE). 通过低秩表示寻找数据在低秩子空间下的表示

降低噪声的影响从而提高算法的鲁棒性

并通过自适应图嵌入方法

使选择特征保持原有的局部关系. 实验结果表明

本文提出算法的分类准确率优于其他对比算法.

Abstract

Dimensionality reduction becomes a significant problem due to the proliferation of high dimensional data. For dimensionality reduction

feature selection is more analytical than feature extraction. Therefore feature selection plays an important role in pattern recognition. In recent years

many feature selection methods based on manifold learning have been proposed. However

such methods are susceptible to noise data. Therefore

an efficient unsupervised feature selection method is proposed－joint low-rank representation and graph embedding for unsupervised feature selection (JLRRGE). This method not only finds the low rank structure of the data after selecting feature

which makes the algorithm more robust

but also preserves the local manifold structure of the data through the adaptive graph embedding. The experimental results show that the proposed algorithm is superior to other compared methods.

关键词

Keywords

references

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