Hierarchical matrix approximation of large covariance matrices

Handle URI:
http://hdl.handle.net/10754/623621
Title:
Hierarchical matrix approximation of large covariance matrices
Authors:
Litvinenko, Alexander ( 0000-0001-5427-3598 ) ; Genton, Marc G. ( 0000-0001-6467-2998 ) ; Sun, Ying ( 0000-0001-6703-4270 ) ; Tempone, Raul Fidel ( 0000-0003-1967-4446 )
Abstract:
We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(nlogn). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and op- timal design.
KAUST Department:
Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ); Spatio-Temporal Statistics and Data Analysis Group; Environmental Statistics Group, KAUST
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
Issue Date:
5-Jan-2015
Type:
Poster
Sponsors:
SRI UQ, KAUST
Additional Links:
https://sri-uq.kaust.edu.sa/Pages/UQAnnualWorkshop2015.aspx
Appears in Collections:
Posters

Full metadata record

DC FieldValue Language
dc.contributor.authorLitvinenko, Alexanderen
dc.contributor.authorGenton, Marc G.en
dc.contributor.authorSun, Yingen
dc.contributor.authorTempone, Raul Fidelen
dc.date.accessioned2017-05-16T08:19:36Z-
dc.date.available2017-05-16T08:19:36Z-
dc.date.issued2015-01-05-
dc.identifier.urihttp://hdl.handle.net/10754/623621-
dc.description.abstractWe approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(nlogn). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and op- timal design.en
dc.description.sponsorshipSRI UQ, KAUSTen
dc.relation.urlhttps://sri-uq.kaust.edu.sa/Pages/UQAnnualWorkshop2015.aspxen
dc.subjecthierarchical matricesen
dc.subjectapproximate covarianceen
dc.subjectKrigingen
dc.subjectgeostatistical optimal designen
dc.subjectweather forecastingen
dc.titleHierarchical matrix approximation of large covariance matricesen
dc.typePosteren
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)en
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Groupen
dc.contributor.departmentEnvironmental Statistics Group, KAUSTen
dc.conference.dateJanuary 2015en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)en
dc.conference.locationKAUST, B1en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.