The Library
DECOrrelated feature space partitioning for distributed sparse regression
Tools
Wang, Xiangyu, Dunson, David B. and Leng, Chenlei (2016) DECOrrelated feature space partitioning for distributed sparse regression. In: 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, 5-10 Dec 2016. Published in: Advances in Neural Information Processing Systems (NIPS 2016), 29 ISSN 1049-5258.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: https://papers.nips.cc/paper/6349-decorrelated-fea...
Abstract
Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space). While the majority of the literature focuses on sample space partitioning, feature space partitioning is more effective when p >> n. Existing methods for partitioning features, however, are either vulnerable to high correlations or inefficient in reducing the model dimension. In this paper, we solve these problems through a new embarrassingly parallel framework named DECO for distributed variable selection and parameter estimation. In DECO, variables are first partitioned and allocated to m distributed workers. The decorrelated subset data within each worker are then fitted via any algorithm designed for high-dimensional problems. We show that by incorporating the decorrelation step, DECO can achieve consistent variable selection and parameter estimation on each subset with (almost) no assumptions. In addition, the convergence rate is nearly minimax optimal for both sparse and weakly sparse models and does NOT depend on the partition number m. Extensive numerical experiments are provided to illustrate the performance of the new framework.
Item Type: | Conference Item (Paper) | ||||
---|---|---|---|---|---|
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Advances in Neural Information Processing Systems (NIPS 2016) | ||||
Publisher: | Morgan Kaufmann Publishers, Inc. | ||||
ISSN: | 1049-5258 | ||||
Official Date: | 16 November 2016 | ||||
Dates: |
|
||||
Volume: | 29 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 30th Conference on Neural Information Processing Systems (NIPS 2016) | ||||
Type of Event: | Conference | ||||
Location of Event: | Barcelona, Spain | ||||
Date(s) of Event: | 5-10 Dec 2016 | ||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |