A decomposition for three-way arrays
SIAM Journal on Matrix Analysis and Applications
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
Imposing independence constraints in the CP model
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
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 blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind PARAFAC receivers for DS-CDMA systems
IEEE Transactions on Signal Processing
A Block Component Model-Based Blind DS-CDMA Receiver
IEEE Transactions on Signal Processing
Blind Identification of Underdetermined Mixtures by Simultaneous Matrix Diagonalization
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Parallel factor analysis in sensor array processing
IEEE Transactions on Signal Processing
Blind Deconvolution of DS-CDMA Signals by Means of Decomposition in Rank- Terms
IEEE Transactions on Signal Processing
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Although CPA (canonical/parallel factor analysis) has a unique solution, the actual computation can be made more robust by incorporating extra constraints. In several applications, the factors in one mode are known to be statistically independent. On the other hand, in Independent Component Analysis (ICA), it often makes sense to impose a Khatri-Rao structure on the mixing vectors. In this paper, we propose a new algorithm to impose independence constraints in CPA. Our algorithm implements the algebraic CPA structure and the property of statistical independence simultaneously. Numerical experiments show that our method outperforms in several cases pure CPA, pure ICA, and tensor ICA, a previously proposed method for combining ICA and CPA. We also present a strategy for imposing full or partial symmetry in CPA.