Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets
Abstract
Penalized logistic regression (PLR) is a widely used supervised learning model. In this paper, we consider its applications in large-scale data problems and resort to a stochastic primal-dual approach for solving PLR. In particular, we employ a random sampling technique in the primal step and a multiplicative weights method in the dual step. This technique leads to an optimization method with sublinear dependency on both the volume and dimensionality of training data. We develop concrete algorithms for PLR with ℓ_2-norm and ℓ_1-norm penalties, respectively. Experimental results over several large-scale and high-dimensional datasets demonstrate both efficiency and accuracy of our algorithms.
Cite
Text
Peng et al. "Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33460-3_41Markdown
[Peng et al. "Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/peng2012ecmlpkdd-sublinear/) doi:10.1007/978-3-642-33460-3_41BibTeX
@inproceedings{peng2012ecmlpkdd-sublinear,
title = {{Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets}},
author = {Peng, Haoruo and Wang, Zhengyu and Chang, Edward Y. and Zhou, Shuchang and Zhang, Zhihua},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {553-568},
doi = {10.1007/978-3-642-33460-3_41},
url = {https://mlanthology.org/ecmlpkdd/2012/peng2012ecmlpkdd-sublinear/}
}