Joint Low-Rank Representation and Graph Embedding for Unsupervised Feature Selection

Teng Shao-hua; Feng Zhen-ye; Teng Lu-yao; Fang Xiao-zhao

doi:10.12052/gdutxb.190048

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Joint Low-Rank Representation and Graph Embedding for Unsupervised Feature Selection

更新时间：2026-05-27

- Joint Low-Rank Representation and Graph Embedding for Unsupervised Feature Selection
- Vol. 36, Issue 5, Pages: 7-13(2019)
- 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：
- 基金信息：
- DOI：10.12052/gdutxb.190048
  CLC： TP391.4
- Online First：21 August 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
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

XU Y, FANG X Z, LI X L, et al . Data uncertainty in face recognition [J ] . IEEE Transactions on Cybern etics, 2014, 44(10): 1950-1961.

YAO C, CHENG G. Approximative bayes optimality linear discriminant analysis for Chinese handwriting character recognition [J]. Neuro-computing, 2016, 207: 346-353.

FEI L K, ZHANG B, ZHANG W, et al . Local apparent and latent direction extraction for palm-print recognition [J ] . Information Sciences, 2019, 473: 59-72.

LAN X, MA A J, YUEN P C, et al . Joint sparse representation and robust feature-level fusion for multicue visual tracking [J ] . IEEE Transactions on Image Processing, 2015, 24(12): 5826-5841.

FANG X Z, HAN N, WU J G, et al . Approximate low-rank projection learning for feature extraction [J ] . IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(11): 5228-5241.

TENG S H, ZHENG M, LIU D L. Facing music recommended total variation non-negative matrix decomposition method [J]. Application Research of Computers, 2018, 35(4): 1010-1013.

TENG S H, SONG H, HUO Y X, et al . An incremental learning approach in voice compression via sparse dictionary learning [J ] . Journal of Guangdong Unive rsity of Technology, 2018, 35(3): 29-36.

JA IN, DU IN, MAO J. Statistical pattern recognition: a review [J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2002, 27(11): 1502-1502.

FANG X Z, XU Y, LI X L, et al . Locality and similarity preserving embedding for feature selection [J ] . Neurocomputing, 2014, 128: 304-315.

TENG S H, LU D L, HUO Y X, et al . Classification method based on dimension reduction [J ] . Journal of Guangdong University of Technology, 2017, 34(3): 1-7.

IMOTO S, MIVANO S. A top-r feature selection algorithm for microarray gene expression data [J]. IEEE/ACM Transactions on Computational Bi-ology and Bioinformatics, 2012, 9(3): 754-764.

SHANG C, LI M, FENG S, et al . Feature selection via maximizing global information gain for text classification [J ] . Knowledge-Based Systems, 2013, 54: 298-309.

NIE F P, HUANG H, CAI X, et al. Efficient and robust feature selection via joint ℓ2, 1-norms minimization[C]//Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. Curran Associates Inc. 2010.

FANG X Z, XU Y, LI X, et al . Robust semi-supervised subspace clustering via non-negative low-rank representation [J ] . IEEE Transactions on Cybernetics, 2016, 46(8): 1828-1838.

ZHU X F, LI X L, et al . Robust joint graph sparse coding for unsupervised spectral feature selection [J ] . IEEE Transactions on Neural Networks and Learning Systems, 2017, 6(28): 1263-1275.

CHENG Q, ZHOU H, CHENG J. The fisher-markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data [J]. IEEE Transactions on Software Engineering, 2011, 33(6): 1217-1233.

CAI D, ZHANG C, HE X. Unsupervised feature selection for multi-cluster data[C]//ACM SIGKDD International Conference on Knowledge Discovery Data Mining. USA, Washington, DC: ACM, 2010.

NIE F P, XU D, TSANG W H, et al . Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction [J ] . IEEE Transactions on Image Pro-cessing, 2010, 19(7): 1921-1932.

HOU C P, NIE F P, LI X L, et al . Joint embedding learning and sparse regression: a framework for unsupervised feature selection [J ] . IEEE Transactions on Cybernetics, 2013, 44(6): 793-804.

LI Z, YANG Y, LIU J, et al. Unsupervised feature selection using nonnegative spectral analysis [C]∥Proceedings of 26th AAAI Conference on Artificial Intelligence. Toronto: AAAI Press, 2012: 1026-1032.

QIAN M, ZHAI C. Robust unsupervised feature selection[C]//International Joint Conference on Artificial Intelligence. China, Beijing: ACM, 2013.

SHI L, DU L, SHEN Y D. Robust spectral learning for unsupervised feature selection[C]//IEEE International Conference on Data Mining. China, Shenzhen: IEEE, 2015.

NIE F P, ZHU W, Li X L. Unsupervised feature selection with structured graph optimization[C]//Thirtieth Aaai Conference on Artificial Intelligence. USA, Phoenix: AAAI Press, 2016.

WEN J, HAN N, FANG X Z, et al . Low-rank preserving projection Via graph regularized reconstruction [J ] . IEEE Transactions on Cybernetics, 2018: 1-13.

CANDES E J, RECHT B. Exact matrix completion via convex optimization [J]. Foundations of Computational Mathematics, 2009, 9(6): 717.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Joint Graph Embedding and Feature Weighting for Unsupervised Feature Selection

Unsupervised GAN Multi-focus Image Fusion Based on Attention Mechanism

Multi-view Consistency Learning with Block Diagonal Guidance

Combined Weighting Method for Short Text Features

A Monocular Depth Estimation Combined with Attention and Unsupervised Deep Learning

Related Author

Zhang Zhen-bin

Zhang Wei

Li Weitong

Jiang Yunfeng

Zhang Wei

Teng Luyao

Wei Xiaojie

Teng Shaohua

Related Institution

School of Information Engineering, Guangdong University of Technology

School of Computer Science and Technology, Guangdong University of Technology

School of Information Engineering, Guangzhou Panyu Polytechnic

School of Computers, Guangdong University of Technology

Guangdong Nanfang Media Group

AI问答

⁰