Learning for Larger Datasets with the Gaussian Process Latent Variable Model

Neil D. Lawrence
: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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