Learning for Larger Datasets with the Gaussian Process Latent Variable Model

:243-250, 2007.

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.

Cite this Paper

BibTeX

@InProceedings{pmlr-v-lawrence-larger07,
  title = 	 {Learning for Larger Datasets with the Gaussian Process Latent Variable Model},
  author = 	 {Neil D. Lawrence},
  pages = 	 {243--250},
  year = 	 {},
  editor = 	 {},
  address = 	 {San Juan, Puerto Rico},
  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.}
}

Endnote

%0 Conference Paper
%T Learning for Larger Datasets with the Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B 
%C Proceedings of Machine Learning Research	
%D 
%E 	
%F pmlr-v-lawrence-larger07
%I PMLR	
%J Proceedings of Machine Learning Research	
%P 243--250
%U http://inverseprobability.com
%V 
%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. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.

RIS

TY  - CPAPER
TI  - Learning for Larger Datasets with the Gaussian Process Latent Variable Model
AU  - Neil D. Lawrence
BT  - 
PY  - 
DA  - 
ED  - 	
ID  - pmlr-v-lawrence-larger07
PB  - PMLR	
SP  - 243
DP  - PMLR
EP  - 250
L1  - 
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  -

APA

Lawrence, N.D.. (). Learning for Larger Datasets with the Gaussian Process Latent Variable Model. , in PMLR :243-250

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