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Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Balamurugan, P (2013) Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression. In: IEEE 13th International Conference on Data Mining (ICDM), DEC 07-10, 2013, Dallas, TX, pp. 949-954.

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Official URL: http://dx.doi.org/10.1109/ICDM.2013.126

Abstract

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

Item Type: Conference Proceedings
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Additional Information: copyright for this article belongs to IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 26 May 2014 11:24
Last Modified: 26 May 2014 11:24
URI: http://eprints.iisc.ernet.in/id/eprint/48967

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