Bregman Distance to L1 Regularized Logistic Regression

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.