Gaussian Process Latent Random Field

11Citations
Citations of this article
33Readers
Mendeley users who have this article in their library.

Abstract

The Gaussian process latent variable model (GPLVM) is an unsupervised probabilistic model for nonlinear dimensionality reduction. A supervised extension, called discriminative GPLVM (DGPLVM), incorporates supervisory information into GPLVM to enhance the classification performance. However, its limitation of the latent space dimensionality to at most C − 1 (C is the number of classes) leads to unsatisfactorily performance when the intrinsic dimensionality of the application is higher than C − 1. 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. In GPLRF, the dimensionality of the latent space is no longer restricted to at most C − 1. This makes GPLRF much more flexible than DGPLVM in applications. Experiments conducted on both synthetic and real-world data sets demonstrate that GPLRF performs comparably with DGPLVM and other state-of-the-art methods on data sets with intrinsic dimensionality at most C − 1, and dramatically outperforms DGPLVM on data sets when the intrinsic dimensionality exceeds C − 1.

Cite

CITATION STYLE

APA

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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free