Bounded Gaussian process regression

Bjørn Sand Jensen, Jens Brehm Nielsen, Jan Larsen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We approximate the intractable posterior distributions by the Laplace approximation and expectation propagation and show the properties of the models on an artificial example. We finally consider two real-world data sets originating from perceptual rating experiments which indicate a significant gain obtained with the proposed explicit noise-model extension.
Original languageEnglish
Title of host publication2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages6
PublisherIEEE
Publication date2013
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Southampton, United Kingdom
Duration: 22 Sep 201325 Sep 2013
http://mlsp2013.conwiz.dk

Workshop

Workshop2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
CountryUnited Kingdom
CitySouthampton
Period22/09/201325/09/2013
Internet address
SeriesMachine Learning for Signal Processing
ISSN1551-2541

Keywords

  • Bioengineering
  • Communication, Networking and Broadcast Technologies
  • Computing and Processing
  • General Topics for Engineers
  • Robotics and Control Systems
  • Signal Processing and Analysis
  • Transportation

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