2017/07/31 by Satoshi Hara, Takayuki Katsuki, Hara, Satoshi +10
Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1707.09688
openalex publication_date 2017/07/31 · openalex created_date 2017/08/08 · openalex updated_date 2026/07/28
Two-sample feature selection is the problem of finding features that describe a difference between two probability distributions, which is a ubiquitous problem in both scientific and engineering studies. However, existing methods have limited applicability because of their restrictive assumptions on data distributoins or computational difficulty. In this paper, we resolve these difficulties by formulating the problem as a sparsest k-subgraph problem. The proposed method is nonparametric and does not assume any specific parametric models on the 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.