Large Scale Learning with the Gaussian Process Latent Variable Model

[edit]

Neil D. Lawrence, University of Sheffield

Related Material

Errata

  • Page 14: Sign and inverse wrong on definition of $\mathbf{C}$ just before (4). Corrected in version from December 16, 2008.
    Thanks to: John Guiver

Abstract

In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GP-LVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. We briefly consider a GPR toy problem to highlight the strenghts and weaknesses of the different approaches before studying the perfomance of these techniques on a benchmark visualisation data set.


@TechReport{lawrence-largescale06,
  title = 	 {Large Scale Learning with the Gaussian Process Latent Variable Model},
  author = 	 {Neil D. Lawrence},
  year = 	 {2006},
  institution = 	 {University of Sheffield},
  number =       {CS-06-05},
  month = 	 {00},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2006-01-01-lawrence-largescale06.md},
  url =  	 {http://inverseprobability.com/publications/lawrence-largescale06.html},
  abstract = 	 {In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GP-LVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. We briefly consider a GPR toy problem to highlight the strenghts and weaknesses of the different approaches before studying the perfomance of these techniques on a benchmark visualisation data set.},
  key = 	 {Lawrence:largescale06},
  note = 	 {Document updated on December 16, 2008. Original from February 17th, 2006.},
  linkpdf = 	 {ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmSparse.pdf},
  linksoftware = {https://github.com/SheffieldML/GPmat/},
  group = 	 {gplvm,motion,shefml,dimensional reduction}
 

}
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%X In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GP-LVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. We briefly consider a GPR toy problem to highlight the strenghts and weaknesses of the different approaches before studying the perfomance of these techniques on a benchmark visualisation data set.
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ER  -

Lawrence, N.D.. (2006). Large Scale Learning with the Gaussian Process Latent Variable Model.(CS-06-05):-