AN EXPRESSION-REINFORCED SPARSE SUBSPACE CLUSTERING BY ORTHOGONAL MATCHING PURSUIT

Sparse Subspace Clustering (SSC) is an efficient method for clustering high-dimensional data. Traditional SSC calculates the sparse representation of each point separately to obtain coefficient matrix, might undermine the clustering results due to the potential of representation for interaction being neglected. In this paper, based on orthogonal matching pursuit (OMP), a new module for SSC is developed to release potential of interaction among representations of similar data points for expression reinforcement, and an Expression-Reinforced Max Chain for Interaction (ER-MCI) algorithm is proposed to strengthen the effectiveness of affinity matrix. By finding a specific atom of each point and tracking these atoms to build the chain iteratively on which all atoms would lie in the same subspace with high probability. Experimental results show that our approach achieves better clustering performances compared with other SSC algorithms in terms of clustering accuracy, anti-noise ability and subspace-preserving property, and keeps time efficiency.