Robust non-negative matrix factorization

Abstract

Non-negative matrix factorization (NMF) is a recently popularized technique for learning parts-based, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted. To improve the robustness of NMF, a novel algorithm named robust nonnegative matrix factorization (RNMF) is proposed in this paper. We assume that some entries of the data matrix may be arbitrarily corrupted, but the corruption is sparse. RNMF decomposes the non-negative data matrix as the summation of one sparse error matrix and the product of two non-negative matrices. An efficient iterative approach is developed to solve the optimization problem of RNMF. We present experimental results on two face databases to verify the effectiveness of the proposed method.