Sparse Convolved Gaussian Processes for Multi-output Regression

Mauricio A. ÁlvarezNeil D. Lawrence
,  21:57-64, 2009.

Abstract

We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-alvarez-convolved08, title = {Sparse Convolved Gaussian Processes for Multi-output Regression}, author = {Mauricio A. Álvarez and Neil D. Lawrence}, pages = {57--64}, year = {}, editor = {}, volume = {21}, address = {Cambridge, MA}, url = {http://inverseprobability.com/publications/alvarez-convolved08.html}, abstract = {We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.} }
Endnote
%0 Conference Paper %T Sparse Convolved Gaussian Processes for Multi-output Regression %A Mauricio A. Álvarez %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-alvarez-convolved08 %I PMLR %J Proceedings of Machine Learning Research %P 57--64 %U http://inverseprobability.com %V %W PMLR %X We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.
RIS
TY - CPAPER TI - Sparse Convolved Gaussian Processes for Multi-output Regression AU - Mauricio A. Álvarez AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-alvarez-convolved08 PB - PMLR SP - 57 DP - PMLR EP - 64 L1 - UR - http://inverseprobability.com/publications/alvarez-convolved08.html AB - We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network. ER -
APA
Álvarez, M.A. & Lawrence, N.D.. (). Sparse Convolved Gaussian Processes for Multi-output Regression. , in PMLR :57-64

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