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Nesterov’s Iterations for NMF-Based Supervised Classification of Texture Patterns

  • Rafal Zdunek
  • Zhaoshui He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)

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.

Keywords

Nonnegative Matrix Factorization Texture Pattern Sift Descriptor Landweber Iteration Optimal Gradient Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rafal Zdunek
    • 1
  • Zhaoshui He
    • 2
  1. 1.Institute of Telecommunications, Teleinformatics and AcousticsWroclaw University of TechnologyWroclawPoland
  2. 2.Faculty of AutomationGuangdong University of TechnologyGuangzhouChina

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