Publication
AISTATS 2017
Conference paper

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.

Date

20 Apr 2017

Publication

AISTATS 2017

Authors

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