A graph Laplacian based approach to semi-supervised feature selection for regression problems

Feature selection is a task of fundamental importance for many data mining or machine learning applications, including regression. Surprisingly, most of the existing feature selection algorithms assume the problems to address are either supervised or unsupervised, while supervised and unsupervised samples are often simultaneously available in real-world applications. Semi-supervised feature selection methods are thus necessary, and many solutions have been proposed recently. However, almost all of them exclusively tackle classification problems. This paper introduces a semi-supervised feature selection algorithm which is specifically designed for regression problems. It relies on the notion of Laplacian score, a quantity recently introduced in the unsupervised framework. Experimental results demonstrate the efficiency of the proposed algorithm.