Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Blind identification of out-of-cell users in DS-CDMA
EURASIP Journal on Applied Signal Processing
A comparison of algorithms for fitting the PARAFAC model
Computational Statistics & Data Analysis
Kruskal's condition for uniqueness in Candecomp/Parafac when ranks and k-ranks coincide
Computational Statistics & Data Analysis
SIAM Journal on Matrix Analysis and Applications
| Hi-index | 35.68 |
Unlike low-rank matrix decomposition, which is generically nonunique for rank greater than one, low-rank three-and higher dimensional array decomposition is unique, provided that the array rank is lower than a certain bound, and the correct number of components (equal to array rank) is sought in the decomposition. Parallel factor (PARAFAC) analysis is a common name for low-rank decomposition of higher dimensional arrays. This paper develops Cramer-Rao bound (CRB) results for low-rank decomposition of three- and four-dimensional (3-D and 4-D) arrays, illustrates the behavior of the resulting bounds, and compares alternating least squares algorithms that are commonly used to compute such decompositions with the respective CRBs. Simple-to-check necessary conditions for a unique low-rank decomposition are also provided