Structure-Constrained Symmetric Low-Rank Representation Algorithm for Subspace Clustering

Abstract

The potential subspace structure of high-dimensional data can be obtained by using subspace clustering,but the existing methods can not reveal the characteristics of global low-rank structure and local sparse structure of data at the same time,which limits the clustering performance.This paper proposes a Structure-Constrained Symmetric Low-Rank Representation(SCSLR) algorithm for subspace clustering.The structure constraint and symmetry constraint are introduced into the object function to limit the solution structure of low-rank representation,and a weighted sparse and symmetric low-rank affinity graph is constructed.On this basis,the spectrum clustering method is used to realize efficient subspace clustering.Experimental results show that the proposed algorithm can accurately represent the complex subspace structure.Its average clustering error on two benchmark datasets,Extended Yale B and Hopkins 155,is 1.37% and 1.43% respectively,and its clustering performance is better than that of Low-Rank Representation(LRR),Sparse Subspace Clustering(LSS),Structure-Constrained Symmetric LRR(LRRSC) and other algorithms.