Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Locality sensitive semi-supervised feature selection
Neurocomputing
Graph-Based Iterative Hybrid Feature Selection
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Clustering-Based Feature Selection in Semi-supervised Problems
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Representation of functional data in neural networks
Neurocomputing
Discriminative semi-supervised feature selection via manifold regularization
IEEE Transactions on Neural Networks
BASSUM: A Bayesian semi-supervised method for classification feature selection
Pattern Recognition
Graph Laplacian for semi-supervised feature selection in regression problems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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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.