A Fast Tracking Algorithm for Generalized LARS/LASSO

Keerthi, Sathiya S and Shevade, Shirish (2007) A Fast Tracking Algorithm for Generalized LARS/LASSO. In: IEEE Transactions on Neural Networks, 18 (6). pp. 1826-1830.

PDF fast.pdf - Published Version Restricted to Registered users only Download (254kB) | Request a copy

Abstract

This letter gives an efficient algorithm for tracking the solution curve of sparse logistic regression with respect to the regularization parameter. The algorithm is based on approximating the logistic regression loss by a piecewise quadratic function, using Rosset and Zhu's path tracking algorithm on the approximate problem, and then applying a correction to get to the true path. Application of the algorithm to text classification and sparse kernel logistic regression shows that the algorithm is efficient.

Item Type:	Journal Article
Publication:	IEEE Transactions on Neural Networks
Publisher:	IEEE
Additional Information:	Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords:	Generalized least angle regression (LARS);least absolute shrinkage and selection operator (LASSO);sparse logistic regression.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	17 Dec 2008 14:53
Last Modified:	19 Sep 2010 04:52
URI:	http://eprints.iisc.ac.in/id/eprint/16547

Actions (login required)

View Item