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Nonnegative Matrix Factorization (NMF) Based Supervised Feature Selection and Adaptation

  • Paresh Chandra Barman
  • Soo-Young Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

We proposed a novel algorithm of supervised feature selection and adaptation for enhancing the classification accuracy of unsupervised Nonnegative Matrix Factorization (NMF) feature extraction algorithm. At first the algorithm extracts feature vectors for a given high dimensional data then reduce the feature dimension using mutual information based relevant feature selection and finally adapt the selected NMF features using the proposed Non-negative Supervised Feature Adaptation (NSFA) learning algorithm. The supervised feature selection and adaptation improve the classification performance which is fully confirmed by simulations with text-document classification problem. Moreover, the non-negativity constraint, of this algorithm, provides biologically plausible and meaningful feature.

Keywords

Nonnegative Matrix Factorization Feature Adaptation Feature extraction Feature selection Document classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Paresh Chandra Barman
    • 1
  • Soo-Young Lee
    • 1
  1. 1.Department of Bio and Brain EngineeringBrain Science Research Center (BSRC), KAISTDaejeonKorea

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