Latent block diagonal representation for subspace clustering

Jie Guo, Lai Wei

Abstract: Spectral-type subspace clustering algorithms have attracted wide attention because of their excellent performance displayed in a great deal of applications in machine learning domain. It is critical for spectral-type subspace clustering algorithms to obtain suitable coefficient matrices which could reflect the subspace structures of data sets. In this paper, we propose a latent block diagonal representation clustering algorithm (LBDR). For a data set, the goal of LBDR is to construct a block diagonal and dense coefficient matrix and settle the noise adaptively within the original data set by using dimension reduction technique concurrently. In brief, by seeking the solution of a joint optimization problem, LBDR is capable of finding a suitable coefficient matrix and a projection matrix. Furthermore, a series of experiments conducted on several benchmark databases show that LBDR dominates the related methods.