Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/37254
Title: | A PCA-based Data Prediction Method | Authors: | Daugulis, Peteris Vagale, Vija MANCINI, Emiliano Castiglione, Filippo |
Issue Date: | 2022 | Publisher: | UNIV LATVIA | Source: | BALTIC JOURNAL OF MODERN COMPUTING, 10 (1) , p. 1 -16 | Abstract: | The 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. | Notes: | Daugulis, P (corresponding author), Daugavpils Univ, Daugavpils, Latvia. peteris.daugulis@du.lv; vija.vagale@du.lv; emiliano.mancini@uhasselt.be; filippo.castiglione@cnr.it |
Document URI: | http://hdl.handle.net/1942/37254 | ISSN: | 2255-8942 | e-ISSN: | 2255-8950 | DOI: | 10.22364/bjmc.2022.10.1.01 | ISI #: | WOS:000779690500001 | Category: | A1 | Type: | Journal Contribution | Validations: | vabb 2024 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
10_1_01_Daugulis.pdf | Published version | 566.47 kB | Adobe PDF | View/Open |
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.