Consistent and Efficient Nonparametric Different-Feature Selection
Abstract
Two-sample feature selection is a ubiquitous problem in both scientific and engineering studies. We propose a feature selection method to find features that describe a difference in two probability distributions. The proposed method is nonparametric and does not assume any specific parametric models on data distributions. We show that the proposed method is computationally efficient and does not require any extra computation for model selection. Moreover, we prove that the proposed method provides a consistent estimator of features under mild conditions. Our experimental results show that the proposed method outperforms the current method with regard to both accuracy and computation time.
Cite
Text
Hara et al. "Consistent and Efficient Nonparametric Different-Feature Selection." International Conference on Artificial Intelligence and Statistics, 2017.Markdown
[Hara et al. "Consistent and Efficient Nonparametric Different-Feature Selection." International Conference on Artificial Intelligence and Statistics, 2017.](https://mlanthology.org/aistats/2017/hara2017aistats-consistent/)BibTeX
@inproceedings{hara2017aistats-consistent,
title = {{Consistent and Efficient Nonparametric Different-Feature Selection}},
author = {Hara, Satoshi and Katsuki, Takayuki and Yanagisawa, Hiroki and Ono, Takafumi and Okamoto, Ryo and Takeuchi, Shigeki},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2017},
pages = {130-138},
url = {https://mlanthology.org/aistats/2017/hara2017aistats-consistent/}
}