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Correntropy-Based Document Clustering via Nonnegative Matrix Factorization

  • Tolga Ensari
  • Jan Chorowski
  • Jacek M. Zurada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

Nonnegative Matrix Factorization (NMF) is one of the popular techniques to reduce the number of attributes of the data. It has been also widely used for clustering. Several types of the objective functions have been used for NMF in the literature. In this paper, we propose to maximize the correntropy similarity measure to produce the factorization itself. Correntropy is an entropy-based criterion defined as a nonlinear similarity measure. Following the discussion of minimization of the correntropy function, we use it to cluster document data set and compare its clustering performance with the Euclidean Distance (EucD)-based NMF. The comparison is illustrated with 20-Newsgroups data set. The results show that our approach has better clustering compared with other methods which use EucD as an objective function.

Keywords

Nonnegative Matrix Factorization (NMF) Correntropy Document Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tolga Ensari
    • 1
  • Jan Chorowski
    • 2
  • Jacek M. Zurada
    • 2
  1. 1.Computer Engineering DepartmentIstanbul UniversityIstanbulTurkey
  2. 2.Electrical and Computer EngineeringUniversity of LouisvilleLouisvilleUSA

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