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Selecting among three‐mode principal component models of different types and complexities: A numerical convex hull based method
Open accessAbstract
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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Cited by
The role of social connection on the experience of COVID-19 related post-traumatic growth and stress
Matos, M., McEwan, K., Kanovský, M., Halamová, J., Steindl, S. R., Ferreira, N., Linharelhos, M., Rijo, D., Asano, K., Vilas, S. P., et al. (2021) · PLOS ONE · doi:10.1371/journal.pone.0261384 · Cited by 73
Metadata
- Authors
- Ceulemans, E., & Kiers, H. A. L.
- Venue
- British Journal of Mathematical and Statistical Psychology
- Publisher
- Wiley
- Published
- 2006-05
- Volume
- 59
- Issue
- 1
- Pages
- 133-150
- ISSN
- 0007-1102 (ISSN-L)
- 2044-8317
- OpenAlex
- W2145319139
- Cited by
- 216
- References
- 0
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- green
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- Ceulemans, E., & Kiers, H. A. L. (2006). Selecting among three‐mode principal component models of different types and complexities: A numerical convex hull based method. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1348/000711005x64817
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