Distributed training of Large-scale Logistic models

Siddharth Gopal, Yiming Yang
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(2):289-297, 2013.

Abstract

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). 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-gopal13, title = {Distributed training of Large-scale Logistic models}, author = {Gopal, Siddharth and Yang, Yiming}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {289--297}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/gopal13.pdf}, url = {https://proceedings.mlr.press/v28/gopal13.html}, abstract = {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). 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.} }
Endnote
%0 Conference Paper %T Distributed training of Large-scale Logistic models %A Siddharth Gopal %A Yiming Yang %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-gopal13 %I PMLR %P 289--297 %U https://proceedings.mlr.press/v28/gopal13.html %V 28 %N 2 %X 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). 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.
RIS
TY - CPAPER TI - Distributed training of Large-scale Logistic models AU - Siddharth Gopal AU - Yiming Yang BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-gopal13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 2 SP - 289 EP - 297 L1 - http://proceedings.mlr.press/v28/gopal13.pdf UR - https://proceedings.mlr.press/v28/gopal13.html AB - 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). 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. ER -
APA
Gopal, S. & Yang, Y.. (2013). Distributed training of Large-scale Logistic models. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(2):289-297 Available from https://proceedings.mlr.press/v28/gopal13.html.

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