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Semiparametric latent factor models

Abstract:: We propose a semiparametric model for regression problems involving multiple response variables. The model makes use of a set of Gaussian processes that are linearly mixed to capture dependencies that may exist among the response variables. We propose an efficient approximate inference scheme for this semiparametric model whose complexity is linear in the number of training data points. We present experimental results in the domain of multi-joint robot arm dynamics.

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APA Style

Teh, Y., Seeger, M., & Jordan, M. (2005). Semiparametric latent factor models. AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, 333–340.

MLA Style

Teh, Y, et al. “Semiparametric Latent Factor Models.” AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, 2005, pp. 333–40.

Chicago Style

Teh, Y, M Seeger, and M Jordan. 2005. “Semiparametric Latent Factor Models.” AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, 333–40.
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Authors

+ Teh, Y More by this author

Role:: Author

+ Seeger, M More by this author

Role:: Author

+ Jordan, M More by this author

Role:: Author

Journal:: AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics More from this journal
Pages:: 333-340
Publication date:: 2005-01-01

Language:: English
Pubs id:: pubs:353277
UUID:: uuid:b3db5a43-e1b9-4e2b-b94b-9a5123cd280e
Local pid:: pubs:353277
Source identifiers:: 353277
Deposit date:: 2013-11-16
ARK identifier:: ark:/29072/ora_b3db5a43e1b94e2bb94b9a5123cd280e

Terms of use

Copyright date:: 2005

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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