Graph Laplacian for semi-supervised feature selection in regression problems

Authors:
Gauthier Doquire;Michel Verleysen
Affiliations:
Université catholique de Louvain, ICTEAM - Machine Learning Group, Louvain-la-Neuve, Belgium;Université catholique de Louvain, ICTEAM - Machine Learning Group, Louvain-la-Neuve, Belgium
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Year:
2011

Citing 6
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Abstract

Feature selection is fundamental in many data mining or machine learning applications. Most of the algorithms proposed for this task make the assumption that the data are either supervised or unsupervised, while in practice supervised and unsupervised samples are often simultaneously available. Semi-supervised feature selection is thus needed, and has been studied quite intensively these past few years almost exclusively for classification problems. In this paper, a supervised then a semi-supervised feature selection algorithms specially designed for regression problems are presented. Both are based on the Laplacian Score, a quantity recently introduced in the unsupervised framework. Experimental evidences show the efficiency of the two algorithms.