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 = {Neil D. Lawrence}, year = {2006}, number = {CS-06-05}, 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. } }
Endnote
%0 Generic %T Large Scale Learning with the Gaussian Process Latent Variable Model %A Neil D. Lawrence %D 2006 %F Lawrence:largescale06 %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.
RIS
TY - GEN TI - Large Scale Learning with the Gaussian Process Latent Variable Model AU - Neil D. Lawrence DA - 2006/01/01 ID - Lawrence:largescale06 IS - CS-06-05 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 -
APA
Lawrence, N.D.. (2006). Large Scale Learning with the Gaussian Process Latent Variable Model. (CS-06-05)

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