PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Iso-lambda Descent Algorithm for the LASSO
Manuel Loth and Philippe Preux
In: ICONIP 2010, 22-25 Nov 2010, Sydney, Australia.

Abstract

Following the introduction by Tibshirani of the LASSO technique for feature selection in regression, two algorithms were proposed by Osborne et al. for solving the associated problem. One is an homotopy method that gained popularity as the LASSO modification of the LARS algorithm. The other is a finite-step descent method that follows a path on the constraint polytope, and seems to have been largely ignored. One of the reason may be that it solves the constrained formulation of the LASSO, as opposed to the more practical regularized formulation. We give here an adaptation of this algorithm that solves the regularized problem, has a simpler formulation, and outperforms state-of-the-art algorithms in terms of speed.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7034
Deposited By:Philippe Preux
Deposited On:12 December 2010