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.
CITATION STYLE
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
Mendeley helps you to discover research relevant for your work.