.png){kind=icon}
.png){kind=icon}
返回
Faster constrained linear regression via two-step preconditioning
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2019.07.070.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we study the large scale constrained linear regression problem and propose a two-step preconditioning method, which is based on some recent developments on random projection, sketching techniques and convex optimization methods. Combining the method with (accelerated) mini-batch SGD, we can achieve an approximate solution with a time complexity lower than that of the state-of-the-art techniques for the low precision case. Our idea can also be extended to the high precision case, which gives an alternative implementation to the Iterative Hessian Sketch (IHS) method with significantly improved time complexity. Experiments on benchmark and synthetic datasets suggest that our methods indeed outperform existing ones considerably in both the low and high precision cases. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Linear regression
Gradient descent
Random projection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。