Robust Semi-supervised Nonnegative Matrix Factorization.

Abstract

Nonnegative matrix factorization (NMF), which aims at finding parts-based representations of nonnegative data, has been widely applied to a wide range of applications such as data clustering, pattern recognition and computer vision. Real-world data are often sparse and noisy which may reduce the accuracy of representations. And a small part of data may have prior label information, which, if utilized, may improve the discriminability of representations. In this paper, we propose a robust semi-supervised nonnegative matrix factorization (RSSNMF) approach which takes all factors into consideration. RSSNMF incorporates the label information as an additional constraint to guarantee that the data with the same label have the same representation. It addresses the sparsity of data and accommodates noises and outliers consistently via L2;1-norm. An iterative updating optimization scheme is derived to solve RSSNMF’s objective function. We have proven the convergence of this optimization scheme by utilizing auxiliary function method and the correctness based on the Karush-Kohn-Tucker condition of optimization theory. Experiments carried on well-known data sets demonstrate the effectiveness of RSSNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information