Teh, Yee-Whye
Seeger, Matthias
Jordan, Michael
Semiparametric Latent Factor Models
http://infoscience.epfl.ch/record/161317/files/aistats05.pdf
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.
2010-12-01T22:25:56Z
http://infoscience.epfl.ch/record/161317
http://infoscience.epfl.ch/record/161317
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