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  Sparse regression via a trust-region proximal method

Kim, D., Sra, S., & Dhillon, I. (2010). Sparse regression via a trust-region proximal method. Poster presented at 24th European Conference on Operational Research (EURO XXIV), Lisboa, Portugal.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C0DA-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-AA2D-D
Genre: Poster

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Kim, D, Author
Sra, S1, 2, Author              
Dhillon, I, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We present a method for sparse regression problems. Our method is based on the nonsmooth trust-region framework that minimizes a sum of smooth convex functions and a nonsmooth convex regularizer. By employing a separable quadratic approximation to the smooth part, the method enables the use of proximity operators, which in turn allow tackling the nonsmooth part efficiently. We illustrate our method by implementing it for three important sparse regression problems. In experiments with synthetic and real-world large-scale data, our method is seen to be competitive, robust, and scalable.

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 Dates: 2010-07
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 6522
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Title: 24th European Conference on Operational Research (EURO XXIV)
Place of Event: Lisboa, Portugal
Start-/End Date: 2010-07-11 - 2010-07-14

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Title: 24th European Conference on Operational Research (EURO XXIV)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 278 Identifier: -