Asynchronous Feature Extraction for Large-Scale Linear Predictors
Abstract
Learning from datasets with a massive number of possible features to obtain more accurate predictors is being intensively studied. In this paper, we aim to perform effective learning by using the L1 regularized risk minimization problems regarding both time and space computational resources. This is accomplished by concentrating on the effective features from among a large number of unnecessary features. To achieve this, we propose a multithreaded scheme that simultaneously runs processes for developing seemingly important features in the main memory and updating parameters regarding only the important features. We verified our method through computational experiments, showing that our proposed scheme can handle terabyte-scale optimization problems with one machine.
Cite
Text
Matsushima. "Asynchronous Feature Extraction for Large-Scale Linear Predictors." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46128-1_38Markdown
[Matsushima. "Asynchronous Feature Extraction for Large-Scale Linear Predictors." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/matsushima2016ecmlpkdd-asynchronous/) doi:10.1007/978-3-319-46128-1_38BibTeX
@inproceedings{matsushima2016ecmlpkdd-asynchronous,
title = {{Asynchronous Feature Extraction for Large-Scale Linear Predictors}},
author = {Matsushima, Shin},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {604-618},
doi = {10.1007/978-3-319-46128-1_38},
url = {https://mlanthology.org/ecmlpkdd/2016/matsushima2016ecmlpkdd-asynchronous/}
}