Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent
Machine Learning
2014-05-16 v1
Abstract
We propose a new two stage algorithm LING for large scale regression problems. LING has the same risk as the well known Ridge Regression under the fixed design setting and can be computed much faster. Our experiments have shown that LING performs well in terms of both prediction accuracy and computational efficiency compared with other large scale regression algorithms like Gradient Descent, Stochastic Gradient Descent and Principal Component Regression on both simulated and real datasets.
Cite
@article{arxiv.1405.3952,
title = {Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent},
author = {Yichao Lu and Dean P. Foster},
journal= {arXiv preprint arXiv:1405.3952},
year = {2014}
}