Parallelizable sparse inverse formulation Gaussian processes (SpInGP)

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5 Citations (Scopus)

Abstract

We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space formulation used in the algorithm, the time complexity of the basic SpInGP is linear, and because all the computations are parallelizable, the parallel form of the algorithm is sublinear in the number of data points. We provide example algorithms to implement the sparse matrix routines and experimentally test the method using both simulated and real data.
Original languageEnglish
Title of host publication2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
PublisherIEEE
Number of pages7
ISBN (Print)978-1-5090-6341-3
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Tokyo, Japan
Duration: 25 Sep 201728 Sep 2017
Conference number: 27
http://mlsp2017.conwiz.dk/home.htm

Publication series

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

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryJapan
CityTokyo
Period25/09/201728/09/2017
Internet address

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