Bregman Distance to L1 Regularized Logistic Regression
Machine Learning
2010-04-23 v1
Abstract
In this work we investigate the relationship between Bregman distances and regularized Logistic Regression model. We present a detailed study of Bregman Distance minimization, a family of generalized entropy measures associated with convex functions. We convert the L1-regularized logistic regression into this more general framework and propose a primal-dual method based algorithm for learning the parameters. We pose L1-regularized logistic regression into Bregman distance minimization and then apply non-linear constrained optimization techniques to estimate the parameters of the logistic model.
Cite
@article{arxiv.1004.3814,
title = {Bregman Distance to L1 Regularized Logistic Regression},
author = {Mithun Das Gupta and Thomas S. Huang},
journal= {arXiv preprint arXiv:1004.3814},
year = {2010}
}
Comments
8 pages, 3 images, shorter version published in ICPR 2008 by same authors.