Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37254
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDaugulis, Peteris-
dc.contributor.authorVagale, Vija-
dc.contributor.authorMANCINI, Emiliano-
dc.contributor.authorCastiglione, Filippo-
dc.date.accessioned2022-05-04T12:34:36Z-
dc.date.available2022-05-04T12:34:36Z-
dc.date.issued2022-
dc.date.submitted2022-05-03T14:59:36Z-
dc.identifier.citationBALTIC JOURNAL OF MODERN COMPUTING, 10 (1) , p. 1 -16-
dc.identifier.urihttp://hdl.handle.net/1942/37254-
dc.description.abstractThe problem of choosing appropriate values for missing data is often encountered in the data science. We describe a novel method containing both traditional mathematics and machine learning elements for prediction (imputation) of missing data. This method is based on the notion of distance between shifted linear subspaces representing the existing data and candidate sets. The existing data set is represented by the subspace spanned by its first principal components. Solutions for the case of the Euclidean metric are given.-
dc.description.sponsorshipThe authors acknowledge partial funding from the following national funding agencies participating in the project MAGIcIAN JPI-AMR (https:// www.magician-amr.eu/) : State Education Development Agency (VIAA, Latvia) and Italian Ministry of Education and Research (MIUR, Italy) .-
dc.language.isoen-
dc.publisherUNIV LATVIA-
dc.titleA PCA-based Data Prediction Method-
dc.typeJournal Contribution-
dc.identifier.epage16-
dc.identifier.issue1-
dc.identifier.spage1-
dc.identifier.volume10-
local.format.pages16-
local.bibliographicCitation.jcatA1-
dc.description.notesDaugulis, P (corresponding author), Daugavpils Univ, Daugavpils, Latvia.-
dc.description.notespeteris.daugulis@du.lv; vija.vagale@du.lv; emiliano.mancini@uhasselt.be;-
dc.description.notesfilippo.castiglione@cnr.it-
local.publisher.placeRAINA BULVARIS 19, RIGA, LV-1586, LATVIA-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.22364/bjmc.2022.10.1.01-
dc.identifier.isiWOS:000779690500001-
local.provider.typewosris-
local.description.affiliation[Daugulis, Peteris; Vagale, Vija] Daugavpils Univ, Daugavpils, Latvia.-
local.description.affiliation[Mancini, Emiliano] Hasselt Univ, Data Sci Inst, Diepenbeek, Belgium.-
local.description.affiliation[Mancini, Emiliano] Amsterdam UMC, Dept Global Hlth, Amsterdam, Netherlands.-
local.description.affiliation[Castiglione, Filippo] Inst Comp Applicat, Rome, Italy.-
local.uhasselt.internationalyes-
item.fulltextWith Fulltext-
item.contributorDaugulis, Peteris-
item.contributorVagale, Vija-
item.contributorMANCINI, Emiliano-
item.contributorCastiglione, Filippo-
item.validationvabb 2024-
item.accessRightsOpen Access-
item.fullcitationDaugulis, Peteris; Vagale, Vija; MANCINI, Emiliano & Castiglione, Filippo (2022) A PCA-based Data Prediction Method. In: BALTIC JOURNAL OF MODERN COMPUTING, 10 (1) , p. 1 -16.-
crisitem.journal.issn2255-8942-
crisitem.journal.eissn2255-8950-
Appears in Collections:Research publications
Files in This Item:
File Description SizeFormat 
10_1_01_Daugulis.pdfPublished version566.47 kBAdobe PDFView/Open
Show simple item record

WEB OF SCIENCETM
Citations

1
checked on Oct 6, 2024

Page view(s)

28
checked on Jun 20, 2022

Download(s)

6
checked on Jun 20, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.