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.
Cite
Text
Teh et al. "Semiparametric Latent Factor Models." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.Markdown
[Teh et al. "Semiparametric Latent Factor Models." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.](https://mlanthology.org/aistats/2005/teh2005aistats-semiparametric/)BibTeX
@inproceedings{teh2005aistats-semiparametric,
title = {{Semiparametric Latent Factor Models}},
author = {Teh, Yee Whye and Seeger, Matthias and Jordan, Michael I.},
booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics},
year = {2005},
pages = {333-340},
volume = {R5},
url = {https://mlanthology.org/aistats/2005/teh2005aistats-semiparametric/}
}