Journal
NEUROCOMPUTING
Volume 121, Issue -, Pages 5-13Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2012.10.028
Keywords
Feature selection; Semi-supervised learning; Graph Laplacian
Categories
Funding
- Belgian FRIA grant
Ask authors/readers for more resources
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. (c) 2013 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available