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|
|Review Status:||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|
|Communicated by:||Holger Fischer|
|Affiliations:||MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)|
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