Nesterov's iterations for NMF-based supervised classification of texture patterns

Abstract

Nonnegative Matrix Factorization (NMF) is an efficient tool for a supervised classification of various objects such as text documents, gene expressions, spectrograms, facial images, and texture patterns. In this paper, we consider the projected Nesterov's method for estimating nonnegative factors in NMF, especially for classification of texture patterns. This method belongs to a class of gradient (first-order) methods but its convergence rate is determined by O(1/k 2). The classification experiments for the selected images taken from the UIUC database demonstrate a high efficiency of the discussed approach. © 2012 Springer-Verlag.