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In this work we investigate the relationship between Bregman distances andregularized Logistic Regression model. We present a detailed study of BregmanDistance minimization, a family of generalized entropy measures associated withconvex functions. We convert the L1-regularized logistic regression into thismore general framework and propose a primal-dual method based algorithm forlearning the parameters. We pose L1-regularized logistic regression intoBregman distance minimization and then apply non-linear constrainedoptimization techniques to estimate the parameters of the logistic model.

Bregman Distance to L1 Regularized Logistic Regression