Stability Selection for Structured Variable Selection release_nno44yqyxfehhmxxv4bpqt4cge

by George Philipp, Seunghak Lee, Eric P. Xing

Released as a article .

2017  

Abstract

In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives. Its benefits were demonstrated when used in conjunction with the lasso and orthogonal matching pursuit algorithms. In this paper, we investigate the applicability of stability selection to structured selection algorithms: the group lasso and the structured input-output lasso. We find that using stability selection often increases the power of both algorithms, but that the presence of complex structure reduces the reliability of error control under stability selection. We give strategies for setting tuning parameters to obtain a good model size under stability selection, and highlight its strengths and weaknesses compared to competing methods screen and clean and cross-validation. We give guidelines about when to use which error control method.
In text/plain format

Archived Files and Locations

application/pdf  478.4 kB
file_g5l7rk3wpva2val7hnahz5l3c4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2017-12-13
Version   v1
Language   en ?
arXiv  1712.04688v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 59e2bf76-33f7-47b3-bb93-d164bc564d87
API URL: JSON