Subspace Clustering via Block-Diagonal Decomposition  

作　　者：Zhiqiang FU Yao ZHAO Dongxia CHANG Yiming WANG 

机构地区：[1]Institute of Information Science,Beijing Jiaotong University,Beijing 100044,China [2]Beijing Key Laboratory of Advanced Information Science and Network Technology,Beijing 100044,China [3]China Construction Bank,Beijing 100032,China

出　　处：《Chinese Journal of Electronics》2024年第6期1373-1382,共10页电子学报（英文版）

基　　金：supported by the National Key R&D Program of China(Grant No.2021ZD0112100);the National Natural Science Fundation of China(Grant No.62120106009);the Fundamental Research Funds for the Central Universities(Grant No.2022JBZY043)。

摘　　要：The subspace clustering has been addressed by learning the block-diagonal self-expressive matrix.This block-diagonal structure heavily affects the accuracy of clustering but is rather challenging to obtain.A novel and effective subspace clustering model,i.e.,subspace clustering via block-diagonal decomposition(SCBD),is proposed,which can simultaneously capture the block-diagonal structure and gain the clustering result.In our model,a strict block-diagonal decomposition is introduced to directly pursue the k block-diagonal structure corresponding to k clusters.In this novel decomposition,the self-expressive matrix is decomposed into the block indicator matrix to demonstrate the cluster each sample belongs to.Based on the strict block-diagonal decomposition,the block-diagonal shift is proposed to capture the local intra-cluster structure,which shifts the samples in the same cluster to get smaller distances and results in more discriminative features for clustering.Extensive experimental results on synthetic and real databases demonstrate the superiority of SCBD over other state-of-the-art methods.

关 键 词：Subspace clustering Representation matrix Low-rank representation 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]