Stochastic methods for ℓ1 regularized loss minimization

Shai Shalev-Shwartz*, Ambuj Tewari

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

We describe and analyze two stochastic methods for ℓ1 regularized loss minimization problems, such as the Lasso. The first method updates the weight of a single feature at each iteration while the second method updates the entire weight vector but only uses a single training example at each iteration. In both methods, the choice of feature/example is uniformly at random. Our theoretical runtime analysis suggests that the stochastic methods should outperform state-of-the-art deterministic approaches, including their deterministic counterparts, when the size of the problem is large. We demonstrate the advantage of stochastic methods by experimenting with synthetic and natural data sets.

Original languageAmerican English
Title of host publicationProceedings of the 26th Annual International Conference on Machine Learning, ICML'09
DOIs
StatePublished - 2009
Externally publishedYes
Event26th Annual International Conference on Machine Learning, ICML'09 - Montreal, QC, Canada
Duration: 14 Jun 200918 Jun 2009

Publication series

NameACM International Conference Proceeding Series
Volume382

Conference

Conference26th Annual International Conference on Machine Learning, ICML'09
Country/TerritoryCanada
CityMontreal, QC
Period14/06/0918/06/09

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