Complex Random Vectors and ICA Models: Identifiability, Uniqueness and Separability
<|reference_start|>Complex Random Vectors and ICA Models: Identifiability, Uniqueness and Separability: In this paper the conditions for identifiability, separability and uniqueness of linear complex valued independent component analysis (ICA) models are established. These results extend the well-known conditions for solving real-valued ICA problems to complex-valued models. Relevant properties of complex random vectors are described in order to extend the Darmois-Skitovich theorem for complex-valued models. This theorem is used to construct a proof of a theorem for each of the above ICA model concepts. Both circular and noncircular complex random vectors are covered. Examples clarifying the above concepts are presented.<|reference_end|>
@article{eriksson2005complex,
    title={Complex Random Vectors and ICA Models: Identifiability, Uniqueness and
  Separability},
    author={Jan Eriksson and Visa Koivunen},
    journal={Information Theory, IEEE Transactions on , vol.52, no.3pp. 1017-
  1029, March 2006},
    year={2005},
    doi={10.1109/TIT.2005.864440},
    archivePrefix={arXiv},
    eprint={cs/0512063},
    primaryClass={cs.IT cs.CE cs.IR cs.LG math.IT}
}