Fast Lasso Algorithm via Selective Coordinate Descent
Abstract
For the AI community, the lasso proposed by Tibshirani is an important regression approach in finding explanatory predictors in high dimensional data. The coordinate descent algorithm is a standard approach to solve the lasso which iteratively updates weights of predictors in a round-robin style until convergence. However, it has high computation cost. This paper proposes Sling, a fast approach to the lasso. It achieves high efficiency by skipping unnecessary updates for the predictors whose weight is zero in the iterations. Sling can obtain high prediction accuracy with fewer predictors than the standard approach. Experiments show that Sling can enhance the efficiency and the effectiveness of the lasso.
Cite
Text
Fujiwara et al. "Fast Lasso Algorithm via Selective Coordinate Descent." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10232Markdown
[Fujiwara et al. "Fast Lasso Algorithm via Selective Coordinate Descent." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/fujiwara2016aaai-fast/) doi:10.1609/AAAI.V30I1.10232BibTeX
@inproceedings{fujiwara2016aaai-fast,
title = {{Fast Lasso Algorithm via Selective Coordinate Descent}},
author = {Fujiwara, Yasuhiro and Ida, Yasutoshi and Shiokawa, Hiroaki and Iwamura, Sotetsu},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {1561-1567},
doi = {10.1609/AAAI.V30I1.10232},
url = {https://mlanthology.org/aaai/2016/fujiwara2016aaai-fast/}
}