Matrix analysis
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Blind source separation via generalized eigenvalue decomposition
The Journal of Machine Learning Research
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Complex blind source separation via simultaneous strong uncorrelating transform
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Blind Separation of Noncircular Correlated Sources Using Gaussian Entropy Rate
IEEE Transactions on Signal Processing
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The linear BSS problem can be solved under certain conditions via a joint diagonalization approach of only two matrices. Algebraic solutions, i.e. solutions that only involve eigenvalue decompositions or singular value decompositions, are of particular interest as efficient eigensolvers exist. Success of these methods depends significantly on particular properties of the sources, such as non-stationarity, non-whiteness, non-Gaussianity, and non-circularity. In this work, we propose alternative algebraic solutions to solve the complex BSS problem, which generalize the existing approaches. For example, applicability of SUT is limited to the positive definiteness of the covariance matrix, whereas our approach allows to exploit alternative information, such as autocorrelation and pseudo-autocorrelation, to solve the complex BBS problem.