Deterministic Feature Selection for Regularized Least Squares Classification
Abstract
We introduce a deterministic sampling based feature selection technique for regularized least squares classification. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We perform experiments on synthetic and real-world datasets, namely a subset of TechTC-300 datasets, to support our theory. Experimental results indicate that the proposed method performs better than the existing feature selection methods.
Cite
Text
Paul and Drineas. "Deterministic Feature Selection for Regularized Least Squares Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44851-9_34Markdown
[Paul and Drineas. "Deterministic Feature Selection for Regularized Least Squares Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/paul2014ecmlpkdd-deterministic/) doi:10.1007/978-3-662-44851-9_34BibTeX
@inproceedings{paul2014ecmlpkdd-deterministic,
title = {{Deterministic Feature Selection for Regularized Least Squares Classification}},
author = {Paul, Saurabh and Drineas, Petros},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {533-548},
doi = {10.1007/978-3-662-44851-9_34},
url = {https://mlanthology.org/ecmlpkdd/2014/paul2014ecmlpkdd-deterministic/}
}