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No penalty no tears : least squares in high-dimensional linear models
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Wang, Xiangyu, Dusnon, David and Leng, Chenlei (2016) No penalty no tears : least squares in high-dimensional linear models. In: 33rd International Conference on Machine Learning, New York City, USA, 19-24 Jun 2016. Published in: Proceedings of the 33rd International Conference on Machine Learning (ICML 2016) pp. 1814-1822. ISBN 1938-7228.
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WRAP_1273119-st-170516-1506.02222v1.pdf - Accepted Version - Requires a PDF viewer. Download (3670Kb) |
Official URL: http://jmlr.org/proceedings/papers/v48/wange16.htm...
Abstract
Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size. For these problems, we advocate the use of a generalized version of OLS motivated by ridge regression, and propose two novel three-step algorithms involving least squares fitting and hard thresholding. The algorithms are methodologically simple to understand intuitively, computationally easy to implement efficiently, and theoretically appealing for choosing models consistently. Numerical exercises comparing our methods with penalization-based approaches in simulations and data analyses illustrate the great potential of the proposed algorithms.
Item Type: | Conference Item (Paper) | ||||||
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Divisions: | Faculty of Science > Statistics | ||||||
Journal or Publication Title: | Proceedings of the 33rd International Conference on Machine Learning (ICML 2016) | ||||||
ISBN: | 1938-7228 | ||||||
Official Date: | 2016 | ||||||
Dates: |
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Date of first compliant deposit: | 20 May 2016 | ||||||
Page Range: | pp. 1814-1822 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 33rd International Conference on Machine Learning | ||||||
Type of Event: | Conference | ||||||
Location of Event: | New York City, USA | ||||||
Date(s) of Event: | 19-24 Jun 2016 | ||||||
Related URLs: | |||||||
Open Access Version: |
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