Characteristic-function-based independent component analysis
Signal Processing - Special section: Security of data hiding technologies
A blind source separation framework for detecting CPM sources mixed by a convolutive MIMO filter
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Blind separation of any source distributions via high-order statistics
Signal Processing
Blind source separation based on cumulants with time and frequency non-properties
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Signal Processing
Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants
IEEE Transactions on Signal Processing
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Multilinear (tensor) ICA and dimensionality reduction
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
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Comon's (1994) well-known scheme for independent component analysis (ICA) is based on the maximal diagonalization, in a least-squares sense, of a higher-order cumulant tensor. In a previous paper, we proved that for fourth-order cumulants, the computation of an elementary Jacobi rotation is equivalent to the computation of the best rank-1 approximation of a fourth-order tensor. In this paper, we show that for third-order tensors, the computation of an elementary Jacobi rotation is again equivalent to a best rank-1 approximation; however, here, it is a matrix that has to be approximated. This favorable computational load makes it attractive to do “something third-order-like” for fourth-order cumulant tensors as well. We show that simultaneous optimal diagonalization of “third-order tensor slices” of the fourth-order cumulant is a suitable strategy. This “simultaneous third-order tensor diagonalization” approach (STOTD) is similar in spirit to the efficient JADE-algorithm