Uniqueness of real and complex linear independent component analysis revisited
Theis FJ (2015)
Publication Type: Conference contribution
Publication year: 2015
Publisher: European Signal Processing Conference, EUSIPCO
Book Volume: 06-10-September-2004
Pages Range: 1705-1708
Conference Proceedings Title: European Signal Processing Conference
Event location: Vienna, AUT
ISBN: 9783200001657
Abstract
Comon showed using the Darmois-Skitovitch theorem that under mild assumptions a real-valued random vector and its linear image are both independent if and only if the linear mapping is the product of a permutation and a scaling matrix. In this work, a much simpler, direct proof is given for this theorem and generalized to the case of random vectors with complex values. The idea is based on the fact that a random vector is independent if and only if locally the Hessian of its logarithmic density is diagonal.
Involved external institutions
Germany (DE)How to cite
APA:
Theis, F.J. (2015). Uniqueness of real and complex linear independent component analysis revisited. In European Signal Processing Conference (pp. 1705-1708). Vienna, AUT: European Signal Processing Conference, EUSIPCO.
MLA:
Theis, F. J.. "Uniqueness of real and complex linear independent component analysis revisited." Proceedings of the 12th European Signal Processing Conference, EUSIPCO 2004, Vienna, AUT European Signal Processing Conference, EUSIPCO, 2015. 1705-1708.
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