N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
Abstract
We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of the non-negative least-angle regression (LARS) called NLARS, where the similarity between input and output is measured through the normalized version of the Hilbert-Schmidt Independence Criterion (HSIC). An advantage of NLARS is that it can easily incorporate with map-reduce frameworks such as Hadoop and Spark. Thus, with the help of distributed computing, a set of features can be efficiently selected from a large and high-dimensional data. Moreover, NLARS is a convex method and can find a global optimum solution. The effectiveness of the proposed method is first demonstrated through feature selection experiments for classification and regression with small and high-dimensional datasets. Finally, we evaluate our proposed method over a large and high-dimensional biology dataset.
Keywords
Cite
@article{arxiv.1411.2331,
title = {N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data},
author = {Makoto Yamada and Avishek Saha and Hua Ouyang and Dawei Yin and Yi Chang},
journal= {arXiv preprint arXiv:1411.2331},
year = {2014}
}
Comments
arXiv admin note: text overlap with arXiv:1202.0515