Parallel Boosting with Momentum
Abstract
We describe a new, simplified, and general analysis of a fusion of Nesterov’s accelerated gradient with parallel coordinate descent. The resulting algorithm, which we call BOOM, for boo sting with m omentum, enjoys the merits of both techniques. Namely, BOOM retains the momentum and convergence properties of the accelerated gradient method while taking into account the curvature of the objective function. We describe a distributed implementation of BOOM which is suitable for massive high dimensional datasets. We show experimentally that BOOM is especially effective in large scale learning problems with rare yet informative features.
Cite
Text
Mukherjee et al. "Parallel Boosting with Momentum." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40994-3_2Markdown
[Mukherjee et al. "Parallel Boosting with Momentum." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/mukherjee2013ecmlpkdd-parallel/) doi:10.1007/978-3-642-40994-3_2BibTeX
@inproceedings{mukherjee2013ecmlpkdd-parallel,
title = {{Parallel Boosting with Momentum}},
author = {Mukherjee, Indraneel and Canini, Kevin Robert and Frongillo, Rafael M. and Singer, Yoram},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {17-32},
doi = {10.1007/978-3-642-40994-3_2},
url = {https://mlanthology.org/ecmlpkdd/2013/mukherjee2013ecmlpkdd-parallel/}
}