Constrained Laplacian Score for Semi-Supervised Feature Selection
Abstract
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.
Cite
Text
Benabdeslem and Al-Hindawi. "Constrained Laplacian Score for Semi-Supervised Feature Selection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011. doi:10.1007/978-3-642-23780-5_23Markdown
[Benabdeslem and Al-Hindawi. "Constrained Laplacian Score for Semi-Supervised Feature Selection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011.](https://mlanthology.org/ecmlpkdd/2011/benabdeslem2011ecmlpkdd-constrained/) doi:10.1007/978-3-642-23780-5_23BibTeX
@inproceedings{benabdeslem2011ecmlpkdd-constrained,
title = {{Constrained Laplacian Score for Semi-Supervised Feature Selection}},
author = {Benabdeslem, Khalid and Al-Hindawi, Mohammed M.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2011},
pages = {204-218},
doi = {10.1007/978-3-642-23780-5_23},
url = {https://mlanthology.org/ecmlpkdd/2011/benabdeslem2011ecmlpkdd-constrained/}
}