Adaptive algorithms to track the PARAFAC decomposition of a third-order tensor
IEEE Transactions on Signal Processing
Nonorthogonal approximate joint diagonalization with well-conditioned diagonalizers
IEEE Transactions on Neural Networks
Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants
IEEE Transactions on Signal Processing
Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures
IEEE Transactions on Audio, Speech, and Language Processing
Speech separation via parallel factor analysis of cross-frequency covariance tensor
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
A combination of parallel factor and independent component analysis
Signal Processing
SIAM Journal on Matrix Analysis and Applications
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In this paper, we study simultaneous matrix diagonalization-based techniques for the estimation of the mixing matrix in underdetermined independent component analysis (ICA). This includes a generalization to underdetermined mixtures of the well-known SOBI algorithm. The problem is reformulated in terms of the parallel factor decomposition (PARAFAC) of a higher-order tensor. We present conditions under which the mixing matrix is unique and discuss several algorithms for its computation.