Learning for Larger Datasets with the Gaussian Process Latent Variable Model

Neil D. Lawrence

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Learning for Larger Datasets with the Gaussian Process Latent Variable Model

Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics, Omnipress :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.

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Cite this Paper

BibTeX


@InProceedings{Lawrence:larger07,
  title = 	 {Learning for Larger Datasets with the {G}aussian Process Latent Variable Model},
  author = 	 {Lawrence, Neil D.},
  booktitle = 	 {Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics},
  pages = 	 {243--250},
  year = 	 {2007},
  editor = 	 {Meila, Marina and Shen, Xiaotong},
  address = 	 {San Juan, Puerto Rico},
  publisher =    {Omnipress},
  pdf = 	 {http://proceedings.mlr.press/v2/lawrence07a/lawrence07a.pdf},
  url = 	 {/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 Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics
%D 2007
%E Marina Meila
%E Xiaotong Shen	
%F Lawrence:larger07
%I Omnipress
%P 243--250
%U /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.

RIS


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
DA  - 2007/03/11
ED  - Marina Meila
ED  - Xiaotong Shen	
ID  - Lawrence:larger07
PB  - Omnipress
SP  - 243
EP  - 250
L1  - http://proceedings.mlr.press/v2/lawrence07a/lawrence07a.pdf
UR  - /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.. (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 Available from /publications/lawrence-larger07.html.