Sparse convolved Gaussian processes for multi-output regression
M Alvarez, N Lawrence - Advances in neural information …, 2008 - proceedings.neurips.cc
Advances in neural information processing systems, 2008•proceedings.neurips.cc
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 …
(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 …
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.
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