Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Abstract
Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classification problems in high dimensions. However, current algorithms for training sparse classifiers typically scale quite unfavorably with respect to the number of training examples. This paper proposes online and multi-pass algorithms for training sparse linear classifiers for high dimensional data. These algorithms have computational complexity and memory requirements that make learning on massive data sets feasible. The central idea that makes this possible is a straightforward quadratic approximation to the likelihood function.
Cite
Text
Balakrishnan and Madigan. "Algorithms for Sparse Linear Classifiers in the Massive Data Setting." Journal of Machine Learning Research, 2008.Markdown
[Balakrishnan and Madigan. "Algorithms for Sparse Linear Classifiers in the Massive Data Setting." Journal of Machine Learning Research, 2008.](https://mlanthology.org/jmlr/2008/balakrishnan2008jmlr-algorithms/)BibTeX
@article{balakrishnan2008jmlr-algorithms,
title = {{Algorithms for Sparse Linear Classifiers in the Massive Data Setting}},
author = {Balakrishnan, Suhrid and Madigan, David},
journal = {Journal of Machine Learning Research},
year = {2008},
pages = {313-337},
volume = {9},
url = {https://mlanthology.org/jmlr/2008/balakrishnan2008jmlr-algorithms/}
}