Minimum Error Entropy Based Sparse Representation for Robust Subspace Clustering

Abstract

This paper addresses the problem of clustering data points that are approximately drawn from multiple subspaces. Recently a large family of spectral clustering based methods, such as sparse subspace clustering (SSC) and low-rank representation (LRR), have been proposed. In this paper, we present a general formulation to unify many of them within a common framework based on atomic representation. Since mean square error (MSE) relies heavily on the Gaussianity assumption, the previous MSE based subspace clustering methods have the limitation of being sensitive to non-Gaussian noise. In this paper, we develop a novel subspace clustering method, termed MEESSC, by specifying the minimum error entropy (MEE) as the loss function and the sparsity inducing atomic set. We show that MEESSC can well overcome the above limitation. The experimental results on both synthetic and real data verify the effectiveness of the proposed method.