DocumentCode :
1062718
Title :
Kruskal´s permutation lemma and the identification of CANDECOMP/PARAFAC and bilinear models with constant modulus constraints
Author :
Jiang, Tao ; Sidiropoulos, Nicholas D.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA
Volume :
52
Issue :
9
fYear :
2004
Firstpage :
2625
Lastpage :
2636
Abstract :
CANDECOMP/PARAFAC (CP) analysis is an extension of low-rank matrix decomposition to higher-way arrays, which are also referred to as tensors. CP extends and unifies several array signal processing tools and has found applications ranging from multidimensional harmonic retrieval and angle-carrier estimation to blind multiuser detection. The uniqueness of CP decomposition is not fully understood yet, despite its theoretical and practical significance. Toward this end, we first revisit Kruskal´s permutation lemma, which is a cornerstone result in the area, using an accessible basic linear algebra and induction approach. The new proof highlights the nature and limits of the identification process. We then derive two equivalent necessary and sufficient uniqueness conditions for the case where one of the component matrices involved in the decomposition is full column rank. These new conditions explain a curious example provided recently in a previous paper by Sidiropoulos, who showed that Kruskal´s condition is in general sufficient but not necessary for uniqueness and that uniqueness depends on the particular joint pattern of zeros in the (possibly pretransformed) component matrices. As another interesting application of the permutation lemma, we derive a similar necessary and sufficient condition for unique bilinear factorization under constant modulus (CM) constraints, thus providing an interesting link to (and unification with) CP.
Keywords :
array signal processing; identification; linear algebra; matrix decomposition; multiuser detection; CANDECOMP/PARAFAC identification; Kruskal permutation lemma; angle-carrier estimation; array signal processing; bilinear models; blind multiuser detection; constant modulus constraints; higher-way arrays; low-rank matrix decomposition; multidimensional harmonic retrieval; Array signal processing; Laboratories; Linear algebra; Matrix decomposition; Multidimensional signal processing; Multiuser detection; Signal processing; Signal processing algorithms; Sufficient conditions; Tensile stress; CANDECOMP; PARAFAC; SVD; constant modulus; identifiablity; three-way array analysis; uniqueness;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2004.832022
Filename :
1323268
Link To Document :
بازگشت