Sufficient conditions for uniqueness in Candecomp/Parafac and Indscal with random component matrices
A Stegeman, JMFT Berge, LD Lathauwer - Psychometrika, 2006 - Springer
A Stegeman, JMFT Berge, LD Lathauwer
Psychometrika, 2006•SpringerA key feature of the analysis of three-way arrays by Candecomp/Parafac is the essential
uniqueness of the trilinear decomposition. We examine the uniqueness of the
Candecomp/Parafac and Indscal decompositions. In the latter, the array to be decomposed
has symmetric slices. We consider the case where two component matrices are randomly
sampled from a continuous distribution, and the third component matrix has full column rank.
In this context, we obtain almost sure sufficient uniqueness conditions for the …
uniqueness of the trilinear decomposition. We examine the uniqueness of the
Candecomp/Parafac and Indscal decompositions. In the latter, the array to be decomposed
has symmetric slices. We consider the case where two component matrices are randomly
sampled from a continuous distribution, and the third component matrix has full column rank.
In this context, we obtain almost sure sufficient uniqueness conditions for the …
Abstract
A key feature of the analysis of three-way arrays by Candecomp/Parafac is the essential uniqueness of the trilinear decomposition. We examine the uniqueness of the Candecomp/Parafac and Indscal decompositions. In the latter, the array to be decomposed has symmetric slices. We consider the case where two component matrices are randomly sampled from a continuous distribution, and the third component matrix has full column rank. In this context, we obtain almost sure sufficient uniqueness conditions for the Candecomp/Parafac and Indscal models separately, involving only the order of the three-way array and the number of components in the decomposition. Both uniqueness conditions are closer to necessity than the classical uniqueness condition by Kruskal.
Springer