Unsupervised maximum margin feature selection via L 2,1-norm minimization

被引：27

作者：

Yang, Shizhun ^{[1
]}

Hou, Chenping ^{[1
]}

Nie, Feiping ^{[2
]}

Wu, Yi ^{[1
]}

机构：

[1] Natl Univ Def Technol, Dept Math & Syst Sci, Changsha 410073, Hunan, Peoples R China

[2] Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA

来源：

NEURAL COMPUTING & APPLICATIONS | 2012年 / 21卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Feature selection; K-means clustering; Maximum margin criterion; Regularization;

D O I：

10.1007/s00521-012-0827-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this article, we present an unsupervised maximum margin feature selection algorithm via sparse constraints. The algorithm combines feature selection and K-means clustering into a coherent framework. L (2,1)-norm regularization is performed to the transformation matrix to enable feature selection across all data samples. Our method is equivalent to solving a convex optimization problem and is an iterative algorithm that converges to an optimal solution. The convergence analysis of our algorithm is also provided. Experimental results demonstrate the efficiency of our algorithm.

引用

页码：1791 / 1799

页数：9

共 50 条

[1] Unsupervised maximum margin feature selection via L2,1-norm minimization
Shizhun Yang
Chenping Hou
Feiping Nie
Yi Wu
[J]. Neural Computing and Applications, 2012, 21 : 1791 - 1799
[2] Unsupervised Discriminative Feature Selection in a Kernel Space via L2,1-Norm Minimization
Liu, Yang
Wang, Yizhou
[J]. 2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 1205 - 1208
[3] Unsupervised and Supervised Feature Selection for Incomplete Data via L2,1-Norm and Reconstruction Error Minimization
Cai, Jun
Fan, Linge
Xu, Xin
Wu, Xinrong
[J]. APPLIED SCIENCES-BASEL, 2022, 12 (17):
[4] Robust discriminant feature selection via joint L2,1-norm distance minimization and maximization
Yang, Zhangjing
Ye, Qiaolin
Chen, Qiao
Ma, Xu
Fu, Liyong
Yang, Guowei
Yan, He
Liu, Fan
[J]. KNOWLEDGE-BASED SYSTEMS, 2020, 207
[5] Fast unsupervised feature selection with anchor graph and ℓ2,1-norm regularization
Haojie Hu
Rong Wang
Feiping Nie
Xiaojun Yang
Weizhong Yu
[J]. Multimedia Tools and Applications, 2018, 77 : 22099 - 22113
[6] Fast unsupervised feature selection with anchor graph and l2,1-norm regularization
Hu, Haojie
Wang, Rong
Nie, Feiping
Yang, Xiaojun
Yu, Weizhong
[J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2018, 77 (17) : 22099 - 22113
[7] Cost-sensitive feature selection via the l2,1-norm
Zhao, Hong
Yu, Shenglong
[J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2019, 104 : 25 - 37
[8] Robust feature selection via l2,1-norm in finite mixture of regression
Li, Xiangrui
Zhu, Dongxiao
[J]. PATTERN RECOGNITION LETTERS, 2018, 108 : 15 - 22
[9] l2,1-norm minimization based negative label relaxation linear regression for feature selection
Peng, Yali
Sehdev, Paramjit
Liu, Shigang
Lie, Jun
Wang, Xili
[J]. PATTERN RECOGNITION LETTERS, 2018, 116 : 170 - 178
[10] Robust Feature Selection via Simultaneous Capped l2 -Norm and l2,1 -Norm Minimization
Lan, Gongmin
Hou, Chenping
Yi, Dongyun
[J]. PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA), 2016, : 147 - 151

← 1 2 3 4 5 →