Graph constraint-based robust latent space low-rank and sparse subspace clustering

被引：6

作者：

Xiao, Yunjun ^{[1
]}

Wei, Jia ^{[1
]}

Wang, Jiabing ^{[1
]}

Ma, Qianli ^{[1
]}

Zhe, Shandian ^{[2
]}

Tasdizen, Tolga ^{[3
]}

机构：

[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Peoples R China

[2] Univ Utah, Sch Comp, Salt Lake City, UT USA

[3] Univ Utah, Sci Comp & Imaging Inst, Salt Lake City, UT USA

来源：

NEURAL COMPUTING & APPLICATIONS | 2020年 / 32卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Dimension reduction; Low-rank and sparse representation; Subspace clustering; Manifold clustering; SEGMENTATION; ALGORITHM; MIXTURES;

D O I：

10.1007/s00521-019-04317-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recently, low-rank and sparse representation-based methods have achieved great success in subspace clustering, which aims to cluster data lying in a union of subspaces. However, most methods fail if the data samples are corrupted by noise and outliers. To solve this problem, we propose a novel robust method that uses the F-norm for dealing with universal noise and thel1norm or thel2,1norm for capturing outliers. The proposed method can find a low-dimensional latent space and a low-rank and sparse representation simultaneously. To preserve the local manifold structure of the data, we have adopted a graph constraint in our model to obtain a discriminative latent space. Extensive experiments on several face benchmark datasets show that our proposed method performs better than state-of-the-art subspace clustering methods.

引用

页码：8187 / 8204

页数：18