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.

Cite this Paper

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 = {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. }, 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 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. %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 - 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. 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 http://inverseprobability.com/publications/lawrence-largescale06.html. Document updated on December 16, 2008. Original from February 17th, 2006.

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