Learning and Utilizing a Pool of Features in Non-negative Matrix Factorization

  • Tetsuya Yoshida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8210)


Learning and utilizing a pool of features for a given data is important to achieve better performance in data analysis. Since many real world data can be represented as a non-negative data matrix, Non-negative Matrix Factorization (NMF) has recently become popular to deal with data under the non-negativity constraint. However, when the number of features is increased, the constraint imposed on the features can hinder the effective utilization of the learned representation. We conduct extensive experiments to investigate the effectiveness of several state-of-the-art NMF algorithms for learning and utilizing a pool of features over document datasets. Experimental results revealed that coping with the non-orthogonality of features is crucial to achieve a stable performance for exploiting a large number of features in NMF.


Normalize Mutual Information Document Cluster Cluster Assignment Imbalanced Data Imbalanced Dataset 
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 International Publishing Switzerland 2013

Authors and Affiliations

  • Tetsuya Yoshida
    • 1
  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan

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