29th International Conference on Machine Learning - ICML 2012, Edimburgo (Reino Unido). 26 junio - 01 julio 2012
Resumen:
We present a novel approach to the formulation and the resolution of sparse Linear Discriminant Analysis (LDA). Our proposal, is based on penalized Optimal Scoring. It has an exact equivalence with penalized LDA, contrary to the multi-class approaches based on the regression of class indicator that have been proposed so far. Sparsity is obtained thanks to a group-Lasso penalty that selects the same features in all discriminant directions. Our experiments demonstrate that this approach generates extremely parsimonious models without compromising prediction performances. Besides prediction, the resulting sparse discriminant directions are also amenable to low-dimensional representations of data. Our algorithm is highly efficient for medium to large number of variables, and is thus particularly well suited to the analysis of gene expression data.
Publicado en ICML 2012, pp: 1167-1174, ISBN: 978-145031285-1
Fecha de publicación: 2012-06-26.
Cita:
L.F. S. Merchante, Y. Grandvalet, G. Govaert, An efficient approach to sparse linear discriminant analysis, 29th International Conference on Machine Learning - ICML 2012, Edimburgo (Reino Unido). 26 junio - 01 julio 2012. En: ICML 2012: Conference proceedings, vol. Vol. 2, ISBN: 978-145031285-1
