GNMF with Newton-based methods

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


Several variants of Nonnegative Matrix Factorization (NMF) have been proposed for supervised classification of various objects. Graph regularized NMF (GNMF) incorporates the information on the data geometric structure to the training process, which considerably improves the classification results. However, the multiplicative algorithms used for updating the underlying factors may result in a slow convergence of the training process. To tackle this problem, we propose to use the Spectral Projected Gradient (SPG) method that is based on quasi-Newton methods. The results are presented for image classification problems.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings
Number of pages8
Publication statusPublished - 2013
Externally publishedYes
Event23rd International Conference on Artificial Neural Networks, ICANN 2013 - Sofia, Bulgaria
Duration: 10 Sep 201313 Sep 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8131 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Artificial Neural Networks, ICANN 2013


  • Graph-regularized NMF
  • Image classification
  • NMF
  • SPG


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