Semiparametric Latent Factor Models

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.


Publié dans:
Artificial Intelligence and Statistics 10
Présenté à:
Artificial Intelligence and Statistics 10
Année
2005
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 Notice créée le 2010-12-01, modifiée le 2019-08-12

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