Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
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
A comparison of algorithms for fitting the PARAFAC model
Computational Statistics & Data Analysis
Tensor Decompositions and Applications
SIAM Review
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
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 Deconvolution of DS-CDMA Signals by Means of Decomposition in Rank- Terms
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
SIAM Journal on Matrix Analysis and Applications
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The fact that the decomposition of a matrix in a minimal number of rank-1 terms is not unique, leads to a basic indeterminacy in factor analysis. Factors and loadings are only unique under certain assumptions. Working in a multilinear framework has the advantage that the decomposition of a higher-order tensor in a minimal number of rank-1 terms (its Canonical Polyadic Decomposition (CPD)) is unique under mild conditions. We have recently introduced Block Term Decompositions (BTD) of a higher-order tensor. BTDs write a given tensor as a sum of terms that have low multilinear rank, without having to be rank-1. In this paper we explain how BTDs can be used for factor analysis and blind source separation. We discuss links with Canonical Polyadic Analysis (CPA) and Independent Component Analysis (ICA). Different variants of the approach are illustrated with examples.