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. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.
@InProceedings{lawrence-larger07,
title = {Learning for Larger Datasets with the Gaussian Process Latent Variable Model},
author = {Neil D. Lawrence},
booktitle = {Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics},
pages = {243},
year = {2007},
editor = {Marina Meila and Xiaotong Shen},
address = {San Juan, Puerto Rico},
month = {00},
publisher = {Omnipress},
edit = {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2007-01-01-lawrence-larger07.md},
url = {http://inverseprobability.com/publications/lawrence-larger07.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. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.},
crossref = {Meila:aistats07},
key = {Lawrence:larger07},
linkpdf = {ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmLarger.pdf},
linksoftware = {https://github.com/SheffieldML/GPmat/},
group = {shefml,gp,spgp,gplvm,dimensional reduction}
}
%T Learning for Larger Datasets with the Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B
%C Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics
%D
%E Marina Meila and Xiaotong Shen
%F lawrence-larger07
%I Omnipress
%P 243--250
%R
%U http://inverseprobability.com/publications/lawrence-larger07.html
%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. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.
TY - CPAPER
TI - Learning for Larger Datasets with the Gaussian Process Latent Variable Model
AU - Neil D. Lawrence
BT - Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics
PY - 2007/01/01
DA - 2007/01/01
ED - Marina Meila
ED - Xiaotong Shen
ID - lawrence-larger07
PB - Omnipress
SP - 243
EP - 250
L1 - ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmLarger.pdf
UR - http://inverseprobability.com/publications/lawrence-larger07.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. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.
ER -
Lawrence, N.D.. (2007). Learning for Larger Datasets with the Gaussian Process Latent Variable Model. Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics :243-250