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

作     者：Xu, Yesong Hu, Ping Dai, Jiashu Yan, Nan Wang, Jun 

作者机构：Anhui Polytech Univ Sch Comp & Informat Wuhu 241000 Peoples R China 

出 版 物：《IEEE SIGNAL PROCESSING LETTERS》 (IEEE Signal Process Lett)

年 卷 期：2024年第31卷

页      面：1154-1158页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Natural Science Foundation of China 

主　　题：Noise Sparse matrices Robustness Clustering algorithms Vectors Optimization Minimization Block diagonal representation low-rank representation subspace clustering noise 

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.