Sparseness and Correntropy-Based Block Diagonal Representation for Robust Subspace Clustering

被引：1

作者：

Xu, Yesong ^{[1
]}

Hu, Ping ^{[1
]}

Dai, Jiashu ^{[1
]}

Yan, Nan ^{[1
]}

Wang, Jun ^{[1
]}

机构：

[1] Anhui Polytech Univ, Sch Comp & Informat, Wuhu 241000, Peoples R China

来源：

IEEE SIGNAL PROCESSING LETTERS | 2024年 / 31卷

基金：

中国国家自然科学基金;

关键词：

Noise; Sparse matrices; Robustness; Clustering algorithms; Vectors; Optimization; Minimization; Block diagonal representation; low-rank representation; subspace clustering; noise; SEGMENTATION;

D O I：

10.1109/LSP.2024.3388967

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Block diagonal representation, which aims to compel the desired representation coefficient to have a block diagonal structure directly, has extensive applications in the domains of computer vision and machine learning. However, single residual modeling in existing works is not robust enough when handling complex noise (i.e., sparse noise and impulsive noise) in reality. To overcome this challenge, a novel Sparseness and Correntropy-based Block Diagonal Representation (SC-BDR) model is proposed, which is able to pursue ideal block diagonal representation and effectively deal with various types of noise. Furthermore, the corresponding optimization algorithm is designed for the proposed problem, and we also conduct extensive experiments to demonstrate the robustness and effectiveness of the SC-BDR model on real-world data.

引用

页码：1154 / 1158

页数：5

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