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}
}

%T Large Scale Learning with the Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B
%D
%F lawrence-largescale06
%P --
%R
%U http://inverseprobability.com/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.

TY - CPAPER
TI - Large Scale Learning with the Gaussian Process Latent Variable Model
AU - Neil D. Lawrence
PY - 2006/01/01
DA - 2006/01/01
ID - lawrence-largescale06
SP -
EP -
L1 - ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmSparse.pdf
UR - http://inverseprobability.com/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.
ER -

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

@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}
}

%T Large Scale Learning with the Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B
%D
%F /lawrence-largescale06
%P --
%R
%U http://inverseprobability.com/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.

TY - CPAPER
TI - Large Scale Learning with the Gaussian Process Latent Variable Model
AU - Neil D. Lawrence
PY - 2006/01/01
DA - 2006/01/01
ID - /lawrence-largescale06
SP -
EP -
L1 - ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmSparse.pdf
UR - http://inverseprobability.com/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.
ER -

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