Date of Original Version

6-2013

Type

Conference Proceeding

Journal Title

Journal of Machine Learning Research : Workshop and Conference Proceedings

Volume

28

First Page

289

Last Page

297

Rights Management

Copyright 2013 by the author(s)

Abstract or Description

Regularized Multinomial Logistic regression has emerged as one of the most common methods for performing data classification and analysis. With the advent of large-scale data it is common to find scenarios where the number of possible multinomial outcomes is large (in the order of thousands to tens of thousands) and the dimensionality is high. In such cases, the computational cost of training logistic models or even simply iterating through all the model parameters is prohibitively expensive. In this paper, we propose a training method for large-scale multinomial logistic models that breaks this bottleneck by enabling parallel optimization of the likelihood objective. Our experiments on large-scale datasets showed an order of magnitude reduction in training time.

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Published In

Journal of Machine Learning Research : Workshop and Conference Proceedings, 28, 289-297.