Sparse representation for Gaussian process models

Lehel Csató, Manfred Opper

Research output: Contribution to journalArticlepeer-review

Abstract

We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the model. Experimental results on toy examples and large real-world datasets indicate the efficiency of the approach.
Original languageEnglish
Pages (from-to)444-450
Number of pages7
JournalAdvances in Neural Information Processing Systems
Volume13
Publication statusPublished - 2002
EventAdvances in Neural Information Processing Systems 1994 - Singapore, Singapore
Duration: 16 Nov 199418 Nov 1994

Bibliographical note

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Keywords

  • sparse representation
  • Gaussian Process
  • limitations
  • large data sets
  • Bayesian online algorithm
  • subsample

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