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
@InProceedings{pmlr-v-lawrence-largescale06, title = {Large Scale Learning with the Gaussian Process Latent Variable Model}, author = {Neil D. Lawrence}, year = {}, editor = {}, number = {CS-06-05}, 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.} }
Endnote
%0 Conference Paper %T Large Scale Learning with the Gaussian Process Latent Variable Model %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-largescale06 %I PMLR %J Proceedings of Machine Learning Research %P -- %U http://inverseprobability.com %V %N CS-06-05 %W PMLR %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 - CPAPER TI - Large Scale Learning with the Gaussian Process Latent Variable Model AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-lawrence-largescale06 PB - PMLR SP - DP - PMLR EP - L1 - 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 -
APA
Lawrence, N.D.. (). Large Scale Learning with the Gaussian Process Latent Variable Model. , in PMLR (CS-06-05):-

Related Material