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Selecting among three‐mode principal component models of different types and complexities: A numerical convex hull based method

Selecting among three‐mode principal component models of different types and complexities: A... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png British Journal of Mathematical and Statistical Psychology Wiley

Selecting among three‐mode principal component models of different types and complexities: A numerical convex hull based method

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References (20)

Publisher
Wiley
Copyright
2006 The British Psychological Society
ISSN
0007-1102
eISSN
2044-8317
DOI
10.1348/000711005X64817
pmid
16709283
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

British Journal of Mathematical and Statistical PsychologyWiley

Published: May 1, 2006

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