Feature Elimination in Kernel Machines in moderately high dimensions
Machine Learning
2015-12-29 v2
Abstract
We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features.We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions.We present four case studies to show that the assumptions are met in most practical situations and present simulation results to demonstrate performance of the proposed approach.
Cite
@article{arxiv.1304.5245,
title = {Feature Elimination in Kernel Machines in moderately high dimensions},
author = {Sayan Dasgupta and Yair Goldberg and Michael Kosorok},
journal= {arXiv preprint arXiv:1304.5245},
year = {2015}
}
Comments
50 pages, 5 figures, submitted to Annals of Statistics