Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method
Several three‐mode principal component models can be considered for the modelling of three‐way, three‐mode data, including the Candecomp/Parafac, Tucker3, Tucker2, and Tucker1 models. The following question then may be raised: given a specific data set, which of these models should be selected, and...
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Veröffentlicht in: | British journal of mathematical & statistical psychology 2006-05, Vol.59 (1), p.133-150 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Several three‐mode principal component models can be considered for the modelling of three‐way, three‐mode data, including the Candecomp/Parafac, Tucker3, Tucker2, and Tucker1 models. The following question then may be raised: given a specific data set, which of these models should be selected, and at what complexity (i.e. with how many components)? We address this question by proposing a numerical model selection heuristic based on a convex hull. Simulation results show that this heuristic performs almost perfectly, except for Tucker3 data arrays with at least one small mode and a relatively large amount of error. |
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ISSN: | 0007-1102 2044-8317 |
DOI: | 10.1348/000711005X64817 |