Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Joint Approximate Diagonalization of Positive Definite Hermitian Matrices
SIAM Journal on Matrix Analysis and Applications
Sensitivity Analysis for the Problem of Matrix Joint Diagonalization
SIAM Journal on Matrix Analysis and Applications
Complex ICA using generalized uncorrelating transform
Signal Processing
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Complex random vectors and ICA models: identifiability, uniqueness, and separability
IEEE Transactions on Information Theory
A matrix joint diagonalization approach for complex independent vector analysis
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Algebraic solutions to complex blind source separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Averaging complex subspaces via a Karcher mean approach
Signal Processing
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In this paper, we address the problem of complex blind source separation (BSS), in particular, separation of nonstationary complex signals. It is known that, under certain conditions, complex BSS can be solved effectively by the so-called Strong Uncorrelating Transform (SUT), which simultaneously diagonalizes one Hermitian positive definite and one complex symmetric matrix. Our current work generalizes SUT to simultaneously diagonalize more than two matrices. A Conjugate Gradient (CG) algorithm for computing simultaneous SUT is developed on an appropriate manifold setting of the problem, namely complex oblique projective manifold. Performance of our method, in terms of separation quality, is investigated by several numerical experiments.