Feature selection in weakly coherent matrices
Machine Learning
2018-04-05 v1 Machine Learning
Abstract
A problem of paramount importance in both pure (Restricted Invertibility problem) and applied mathematics (Feature extraction) is the one of selecting a submatrix of a given matrix, such that this submatrix has its smallest singular value above a specified level. Such problems can be addressed using perturbation analysis. In this paper, we propose a perturbation bound for the smallest singular value of a given matrix after appending a column, under the assumption that its initial coherence is not large, and we use this bound to derive a fast algorithm for feature extraction.
Cite
@article{arxiv.1804.01119,
title = {Feature selection in weakly coherent matrices},
author = {Stephane Chretien and Zhen-Wai Olivier Ho},
journal= {arXiv preprint arXiv:1804.01119},
year = {2018}
}
Comments
14 pages, 6 Figures, Accepted for LVA-ICA 2018 Surrey