LOW-RANK APPROXIMATION OF GENERIC p x q x 2 ARRAYS AND DIVERGING COMPONENTS IN THE CANDECOMP/PARAFAC MODEL

We consider the low-rank approximation over the real field of generic p x q x 2 arrays. For all possible combinations of p, q, and R, we present conjectures on the existence of a best rank-R approximation. Our conjectures are motivated by a detailed analysis of the boundary of the set of arrays with at most rank R. We link these results to the Candecomp/Parafac (CP) model for three-way component analysis. Essentially, CP tries to find a best rank-R approximation to a given three-way array. In the case of p x q x 2 arrays, we show (under some regularity condition) that if a best rank-R approximation does not exist, then any sequence of CP updates will exhibit diverging CP components, which implies that several components are highly correlated in all three modes and their component weights become arbitrarily large. This extends Stegeman [Psychometrika, 71 (2006), pp. 483-501], who considers p x p x 2 arrays of rank p + 1 or higher. We illustrate our results by means of simulations.