| Autor: |
Peteris Daugulis, Vija Vagale, Emiliano Mancini, Filippo Castiglione |
| Přispěvatelé: |
Daugulis, Peteris, Vagale, Vija, MANCINI, Emiliano, Castiglione, Filippo |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Popis: |
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. The 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) . |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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