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  Multivariate Regression via Stiefel Manifold Constraints

BakIr, G., Gretton, A., Franz, M., & Schölkopf, B. (2004). Multivariate Regression via Stiefel Manifold Constraints. In DAGM 2004 (pp. 262-269). Berlin, Germany: Springer.

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 Creators:
BakIr, G1, Author           
Gretton, A1, Author           
Franz, M1, Author           
Schölkopf, B1, Author           
Rasmussen, Editor
C., Editor
Bülthoff, Editor
Giese, M. A., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered independently. By contrast, in our approach the feature construction and the regression estimation are performed jointly, directly minimizing a loss function that we specify, subject to a rank constraint. A major advantage of this approach is that the loss is no longer chosen according to the algorithmic requirements, but can be tailored to the characteristics of the task at hand; the features will then be optimal with respect to this objective, and dependence between the outputs can be exploited.

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 Dates: 2004
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: BibTex Citekey: 2845
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Title: DAGM 2004
Place of Event: Tübingen
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Title: DAGM 2004
Source Genre: Proceedings
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 262 - 269 Identifier: -