Gene Selection in Classification Problems via Projections onto a Latent Space

  • Marilena Pillati
  • Cinzia Viroli
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


The analysis of gene expression data involves the observation of a very large number of variables (genes) on a few units (tissues). In such a context the recourse to conventional classification methods may be hard both for analytical and interpretative reasons. In this work a gene selection procedure for classification problems is addressed. The dimensionality reduction is based on the projections of genes along suitable directions obtained by Independent Factor Analysis (IFA). The performances of the proposed procedure are evaluated in the context of both supervised and unsupervised classification problems for different real data sets.


Acute Myeloid Leukemia Latent Space Gene Selection Independent Component Analysis Latent Variable Model 
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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Marilena Pillati
    • 1
  • Cinzia Viroli
    • 1
  1. 1.Statistics DepartmentUniversity of BolognaItaly

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