Large Scale Learning with the Gaussian Process Latent Variable Model

Neil D. Lawrence

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Large Scale Learning with the Gaussian Process Latent Variable Model

Neil D. Lawrence

, 2006.

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.

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BibTeX


@Misc{Lawrence:largescale06,
  title = 	 {Large Scale Learning with the {G}aussian Process Latent Variable Model},
  author = 	 {Lawrence, Neil D.},
  year = 	 {2006},
  number =       {CS-06-05},
  pdf = 	 {https://inverseprobability.com/publications/files/gplvmSparse.pdf},
  url = 	 {/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.
},
  note =         {Document updated on December 16, 2008. Original from February 17th, 2006.}
}

Endnote

%0 Generic
%T Large Scale Learning with the Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%D 2006	
%F Lawrence:largescale06
%U /publications/lawrence-largescale06.html
%N CS-06-05
%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.

%Z Document updated on December 16, 2008. Original from February 17th, 2006.

RIS


TY  - GEN
TI  - Large Scale Learning with the Gaussian Process Latent Variable Model
AU  - Neil D. Lawrence
DA  - 2006/02/17	
ID  - Lawrence:largescale06
IS  - CS-06-05
L1  - https://inverseprobability.com/publications/files/gplvmSparse.pdf
UR  - /publications/lawrence-largescale06.html
AB  - 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.

N1  - Document updated on December 16, 2008. Original from February 17th, 2006.
ER  -

APA


Lawrence, N.D.. (2006). Large Scale Learning with the Gaussian Process Latent Variable Model. (CS-06-05) Available from /publications/lawrence-largescale06.html. Document updated on December 16, 2008. Original from February 17th, 2006.