Matrix analysis
SIAM Journal on Matrix Analysis and Applications
Decompositions of a Higher-Order Tensor in Block Terms—Part I: Lemmas for Partitioned Matrices
SIAM Journal on Matrix Analysis and Applications
Decompositions of a Higher-Order Tensor in Block Terms—Part II: Definitions and Uniqueness
SIAM Journal on Matrix Analysis and Applications
Unsupervised Multiway Data Analysis: A Literature Survey
IEEE Transactions on Knowledge and Data Engineering
Tensor Decompositions and Applications
SIAM Review
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Matrix Analysis and Applications
Blind PARAFAC receivers for DS-CDMA systems
IEEE Transactions on Signal Processing
Blind Identification of Underdetermined Mixtures by Simultaneous Matrix Diagonalization
IEEE Transactions on Signal Processing
Parallel factor analysis in sensor array processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Identifiability results for blind beamforming in incoherentmultipath with small delay spread
IEEE Transactions on Signal Processing
A Constrained Factor Decomposition With Application to MIMO Antenna Systems
IEEE Transactions on Signal Processing
A CANDECOMP/PARAFAC Perspective on Uniqueness of DOA Estimation Using a Vector Sensor Array
IEEE Transactions on Signal Processing
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In this paper, three sufficient conditions are derived for the three-way CANDECOMP/PARAFAC (CP) model, which ensure uniqueness in one of the three modes (“uni-mode-uniqueness”). Based on these conditions, a partial uniqueness condition is proposed which allows collinear loadings in only one mode. We prove that if there is uniqueness in one mode, then the initial CP model can be uniquely decomposed in a sum of lower-rank tensors for which identifiability can be independently assessed. This condition is simpler and easier to check than other similar conditions existing in the specialized literature. These theoretical results are illustrated by numerical examples.