Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
Forward semi-supervised feature selection
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Semi-Supervised Learning
Constraint scores for semi-supervised feature selection: A comparative study
Pattern Recognition Letters
Local-to-global semi-supervised feature selection
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper, we address the problem of semi-supervised feature selection from high-dimensional data. It aims to select the most discriminative and informative features for data analysis. This is a recent addressed challenge in feature selection research when dealing with small labeled data sampled with large unlabeled data in the same set. We present a filter based approach by constraining the known Laplacian score. We evaluate the relevance of a feature according to its locality preserving and constraints preserving ability. The problem is then presented in the spectral graph theory framework with a study of the complexity of the proposed algorithm. Finally, experimental results will be provided for validating our proposal in comparison with other known feature selection methods.