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  Learning low-rank output kernels

Dinuzzo, F., & Fukumizu, K. (2011). Learning low-rank output kernels. In C.-N. Hsu, & W. Lee (Eds.), Asian Conference on Machine Learning, 14-15 November 2011, South Garden Hotels and Resorts, Taoyuan, Taiwain (pp. 181-196). Cambridge, MA, USA: JMLR.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B922-E Version Permalink: http://hdl.handle.net/21.11116/0000-0001-F25D-6
Genre: Conference Paper

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 Creators:
Dinuzzo, F1, Author              
Fukumizu, K, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Output kernel learning techniques allow to simultaneously learn a vector-valued function and a positive semidefinite matrix which describes the relationships between the outputs. In this paper, we introduce a new formulation that imposes a low-rank constraint on the output kernel and operates directly on a factor of the kernel matrix. First, we investigate the connection between output kernel learning and a regularization problem for an architecture with two layers. Then, we show that a variety of methods such as nuclear norm regularized regression, reduced-rank regression, principal component analysis, and low rank matrix approximation can be seen as special cases of the output kernel learning framework. Finally, we introduce a block coordinate descent strategy for learning low-rank output kernels.

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 Dates: 2011-11
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: DinuzzoF2011
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Title: 3rd Asian Conference on Machine Learning (ACML 2011)
Place of Event: Taoyuan, Taiwan
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Title: Asian Conference on Machine Learning, 14-15 November 2011, South Garden Hotels and Resorts, Taoyuan, Taiwain
Source Genre: Proceedings
 Creator(s):
Hsu, C-N, Editor
Lee, WS, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : JMLR
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 181 - 196 Identifier: -

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
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Pages: - Volume / Issue: 20 Sequence Number: - Start / End Page: - Identifier: -