Subtractive Initialization of Nonnegative Matrix Factorizations for Document Clustering

  • Gabriella Casalino
  • Nicoletta Del Buono
  • Corrado Mencar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6857)


Nonnegative matrix factorizations (NMF) have recently assumed an important role in several fields, such as pattern recognition, automated image exploitation, data clustering and so on. They represent a peculiar tool adopted to obtain a reduced representation of multivariate data by using additive components only, in order to learn parts-based representations of data. All algorithms for computing the NMF are iterative, therefore particular emphasis must be placed on a proper initialization of NMF because of its local convergence. The problem of selecting appropriate starting initialization matrices becomes more complex when data possess special meaning, and this is the case of document clustering. In this paper, we present a new initialization method which is based on the fuzzy subtractive scheme and used to generate initial matrices for NMF algorithms. A preliminary comparison of the proposed initialization with other commonly adopted initializations is presented by considering the application of NMF algorithms in the context of document clustering.


Initialization Strategy Document Cluster Alternate Little Square Subtractive Cluster Initial Matrice 
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 2011

Authors and Affiliations

  • Gabriella Casalino
    • 1
  • Nicoletta Del Buono
    • 2
  • Corrado Mencar
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari Aldo MoroBariItaly
  2. 2.Dipartimento di MatematicaUniversità degli Studi di Bari Aldo MoroBariItaly

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