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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents

ID: 198189.0, MPI für biologische Kybernetik / Biologische Kybernetik
Sparse Gaussian Processes: inference, subspace identification and model selection
Authors:Csato, L.; Opper, M.
Editors:Hof, P.M.J. Van der; Wahlberg, B.; Weiland, S.
Date of Publication (YYYY-MM-DD):2003-08
Title of Proceedings:Proceedings
Start Page:1
End Page:6
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Gaussian Process (GP) inference is a probabilistic kernel method where the GP is treated as a latent function. The inference is carried out using the Bayesian online learning and its extension to the more general iterative approach which we call TAP/EP learning.

Sparsity is introduced in this context to make the TAP/EP method applicable to large datasets. We address the prohibitive scaling of the number of parameters by defining a subset of the training data that is used as the support the GP, thus the number of required parameters is independent of the training set, similar to the case of ``Support--'' or ``Relevance--Vectors''.

An advantage of the full probabilistic treatment is that allows the computation of the marginal data likelihood or evidence, leading to hyper-parameter estimation within the GP inference.

An EM algorithm to choose the hyper-parameters is proposed. The TAP/EP learning is the E-step and the M-step then updates the hyper-parameters. Due to the sparse E-step the resulting algorithm does not involve manipulation of large matrices. The presented algorithm is applicable to a wide variety of likelihood functions. We present results of applying the algorithm on classification and nonstandard regression problems for artificial and real datasets.
Comment of the Author/Creator:electronical version; Index ThA02-2
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
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