Decomposition of quantics in sums of powers of linear forms
Signal Processing - Special issue on higher order statistics
A fast exponential decomposition algorithm and its applications to structured matrices
A fast exponential decomposition algorithm and its applications to structured matrices
A Multilinear Singular Value Decomposition
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
SIAM Journal on Matrix Analysis and Applications
Enhanced Line Search: A Novel Method to Accelerate PARAFAC
SIAM Journal on Matrix Analysis and Applications
Tensor Decompositions and Applications
SIAM Review
Discrete-Time Signal Processing
Discrete-Time Signal Processing
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
A Block Component Model-Based Blind DS-CDMA Receiver
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Block component analysis, a new concept for blind source separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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We present a new necessary and sufficient condition for essential uniqueness of the decomposition of a third-order tensor in rank-$(L_r,L_r,1)$ terms. We derive a new deterministic technique for blind signal separation that relies on this decomposition. The method assumes that the signals can be modeled as linear combinations of exponentials or, more generally, as exponential polynomials. The results are illustrated by means of numerical experiments.