Gene Selection in Classification Problems via Projections onto a Latent Space

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.