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A Comparison on Score Spaces for Expression Microarray Data Classification

  • Alessandro Perina
  • Pietro Lovato
  • Marco Cristani
  • Manuele Bicego
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)

Abstract

In this paper an empirical evaluation of different generative scores for expression microarray data classification is proposed. Score spaces represent a quite recent trend in the machine learning community, taking the best of both generative and discriminative classification paradigms. The scores are extracted from topic models, a class of highly interpretable probabilistic tools whose utility in the microarray classification context has been recently assessed. The experimental evaluation, performed on 3 literature datasets and with 7 score spaces, demonstrates the viability of the proposed scheme and, for the first time, it compares pros and cons of each space.

Keywords

Topic Model Latent Dirichlet Allocation Neural Information Processing System Expression Microarray Data Probabilistic Latent Semantic Analysis 
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

  • Alessandro Perina
    • 1
  • Pietro Lovato
    • 2
  • Marco Cristani
    • 2
    • 3
  • Manuele Bicego
    • 2
  1. 1.Microsoft ResearchRedmondUSA
  2. 2.Department of Computer ScienceUniversity of VeronaVeronaItaly
  3. 3.Italian Institute of TechnologyGenoaItaly

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