Projection predictive model selection for Gaussian processes

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Abstract

We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of variables based on their predictive relevance. Our results on synthetic and real world datasets show that the proposed method improves the assessment of variable relevance compared to the automatic relevance determination (ARD) via the length-scale parameters. We expect the method to be useful for improving explainability of the models, reducing the future measurement costs and reducing the computation time for making new predictions.

Details

Original languageEnglish
Title of host publication2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Salerno, Italy
Duration: 13 Sep 201616 Sep 2016
Conference number: 26
http://mlsp2016.conwiz.dk/home.htm

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
ISSN (Print)2161-0371
ISSN (Electronic)2161-0363

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryItaly
CitySalerno
Period13/09/201616/09/2016
Internet address

ID: 9728972