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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Boutsidis, C., Gallopoulos, E.: Svd based initialization: ahead start for nonnegative matrix factorization. Pattern Recognition 41(4), 1350–1362 (2008)CrossRefMATHGoogle Scholar
  2. 2.
    Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.I.: Alternating Least Squares and Related Algorithms for NMF and SCA Problems. In: Nonnegative Matrix and Tensor Factorizations. John Wiley & Sons, UK (2009)CrossRefGoogle Scholar
  3. 3.
    Chiu, S.L.: Fuzzy Model Estimation based on Cluster Estimation. J. Intelligent and Fuzzy Systems 2, 267–278 (1994)Google Scholar
  4. 4.
    Choi, S.: Algorithms for orthogonal nonnegative matrix factorization. Proc. Intern. Joint Conf. Neural Networks (2008)Google Scholar
  5. 5.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Machine Intell. 1(4), 224–227 (1979)CrossRefGoogle Scholar
  6. 6.
    Del Buono, N., Lucarelli, M.: Comparative studies on initializations for nonnegative matrix factorization algorithms, Tech. Rep. 17/10, Univ. Bari, Italy (2010)Google Scholar
  7. 7.
    Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Machine Learning Research 5, 1457–1469 (2004)MathSciNetMATHGoogle Scholar
  8. 8.
    Lazar, C., Doncescu, A., Kabbaj, N.: Non Negative Matrix Factorization clustering capabilities; application on multivariate image segmentation. Int. J. of Business Intel. Data Mining 5(3), 285–296 (2010)CrossRefGoogle Scholar
  9. 9.
    Lee, D.D., Seung, S.H.: Algorithms for non-negative matrix factorization. In: Proc. Adv. Neural Information Proc. Syst. Conf., vol. 13, pp. 556–562 (2000)Google Scholar
  10. 10.
    Shahnaz, F., Berry, M.W., Pauca, M.P., Plemmons, R.J.: Document clustering using nonnegative matrix factorization. Information Processing and Managements: Intern. J. 42(2), 373–386 (2006)CrossRefMATHGoogle Scholar
  11. 11.
    Xu, W., Liu, X., Gong, Y.: Document clustering based on nonnegative matrix factorization. In: Proc. SIGIR, pp. 267–273 (2003)Google Scholar
  12. 12.
    Xue, Y., Tong, C.S., Chen, Y., Chen, W.-S.: Clustering-based initialization for non-negative matrix factorization. Appl. Math. and Comp. 205, 525–536 (2008)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Zhenga, Z., Yang, J.: Initialization enhancer for non-negative matrix factorization. Eng. Appl. Art. Int. 20, 101–110 (2007)CrossRefGoogle Scholar

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

Personalised recommendations