Gaussian Process Latent Random Field

Authors

  • Guoqiang Zhong Chinese Academy of Sciences
  • Wu-Jun Li The Hong Kong University of Science and Technology
  • Dit-Yan Yeung The Hong Kong University of Science and Technology
  • Xinwen Hou Chinese Academy of Sciences
  • Cheng-Lin Liu Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v24i1.7697

Abstract

In this paper, we propose a novel supervised extension of GPLVM, called Gaussian process latent random field (GPLRF), by enforcing the latent variables to be a Gaussian Markov random field with respect to a graph constructed from the supervisory information.

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Published

2010-07-03

How to Cite

Zhong, G., Li, W.-J., Yeung, D.-Y., Hou, X., & Liu, C.-L. (2010). Gaussian Process Latent Random Field. Proceedings of the AAAI Conference on Artificial Intelligence, 24(1), 679-684. https://doi.org/10.1609/aaai.v24i1.7697

Issue

Vol. 24 No. 1 (2010): Twenty-Fourth AAAI Conference on Artificial Intelligence

Section

AAAI Technical Track: Machine Learning