Bregman distance to L1 regularized logistic regression

Mithun Das Gupta, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work we investigate the relationship between Bregman distances and regularized Logistic Regression model. We convert L1-regularized logistic regression (LR) into more general Bregman divergence framework and propose a primal-dual method based algorithm for learning the parameters of the model. The proposed method utilizes L1 regularization to incorporate parameter sparsity into the divergence minimization scheme. We perform tests on public domain data sets and produce results which are amongst the best reported.

Original languageEnglish (US)
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781424421756
DOIs
StatePublished - 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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