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The Typical Rank of Tall Three-Way Arrays

Published online by Cambridge University Press:  01 January 2025

Jos M. F. Ten Berge*
Affiliation:
University of Groningen
*
Requests for reprints should be sent to Jos ten Berge, Heijmans Institute of Psychological Research, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, THE NETHERLANDS. E-mail: [email protected]

Abstract

The rank of a three-way array refers to the smallest number of rank-one arrays (outer products of three vectors) that generate the array as their sum. It is also the number of components required for a full decomposition of a three-way array by CANDECOMP/PARAFAC. The typical rank of a three-way array refers to the rank a three-way array has almost surely. The present paper deals with typical rank, and generalizes existing results on the typical rank of I × J × K arrays with K = 2 to a particular class of arrays with K ≥ 2. It is shown that the typical rank is I when the array is tall in the sense that JK − J < I < JK. In addition, typical rank results are given for the case where I equals JK − J.

Type
Original Paper
Copyright
Copyright © 2000 The Psychometric Society

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Footnotes

The author is obliged to Henk Kiers, Tom Snijders, and Philip Thijsse for helpful comments.

References

Atkinson, M.D., Stevens, N.M. (1979). On the multiplicative complexity of a family of bilinear forms. Linear Algebra and its Applications, 27, 18CrossRefGoogle Scholar
Carroll, J.D., Chang, J.J. (1970). Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition. Psychometrika, 35, 283319CrossRefGoogle Scholar
Fisher, F.M. (1966). The identification problem in Econometrics. New York, NY: McGraw-HillGoogle Scholar
Franc, A. (1992). Etudes algebriques des multitableaux: Apports de l'algebre tensorielle. Unpublished doctoral dissertation, University of Montpellier II.Google Scholar
Harshman, R.L. (1970). Foundations of the PARAFAC procedure: Models and conditions for an “explanatory” multimodal factor analysis. UCLA Working Papers in Phonetics, 16, 184Google Scholar
Kruskal, J.B. (1977). Three-way arrays: Rank and uniqueness of trilinear decompositions with applications to arithmetic complexity and statistics. Linear Algebra and its Applications, 18, 95138CrossRefGoogle Scholar
Kruskal, J.B. (1983). Statement of some current results about three-way arrays. Unpublished manuscript, AT&T Bell Laboratories, Murray Hill, NJ.Google Scholar
Kruskal, J.B. (1989). Rank, decomposition, and uniqueness for 3-way and N-way arrays. In Coppi, R., Bolasco, S. (Eds.), Multiway data analysis (pp. 718). Amsterdam: North-HollandGoogle Scholar
Murakami, T., ten Berge, J.M.F., Kiers, H.A.L. (1998). A case of extreme simplicity of the core matrix in three-mode principal components analysis. Psychometrika, 63, 255261CrossRefGoogle Scholar
Rocci, R., ten Berge, J.M.F. (1994). A simplification of a result by Zellini on the maximal rank of symmetric three-way arrays. Psychometrika, 59, 377380CrossRefGoogle Scholar
ten Berge, J.M.F. (1991). Kruskal's polynomial for 2×2×2 arrays and a generalization to 2×n×n arrays. Psychometrika, 56, 631636CrossRefGoogle Scholar
ten Berge, J.M.F., Kiers, H.A.L. (1999). Simplicity of core arrays in three-way principal component analysis and the typical rank ofP ×Q × 2 arrays. Linear Algebra and its Applications, 294, 169179CrossRefGoogle Scholar
Thijsse, G.P.A. (1994). Simultaneous diagonal forms for pairs of matrices (Report 9450/B). Rotterdam: Econometric InstituteGoogle Scholar