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arXiv · 1004.3814

Bregman Distance to L1 Regularized Logistic Regression

Mithun Das Gupta·Thomas S. Huang
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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.

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Mithun Das Gupta, Thomas S. Huang. 2010-04-21. Bregman Distance to L1 Regularized Logistic Regression. https://arxiv.org/abs/1004.3814

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