A complex generalized Gaussian distribution: characterization, generation, and estimation
IEEE Transactions on Signal Processing
Complex independent component analysis by entropy bound minimization
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Independent component analysis by entropy bound minimization
IEEE Transactions on Signal Processing
Essential statistics and tools for complex random variables
IEEE Transactions on Signal Processing
The complex Gaussian kernel LMS algorithm
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
On entropy rate for the complex domain and its application to i.i.d. sampling
IEEE Transactions on Signal Processing
Algorithms for complex ML ICA and their stability analysis using wirtinger calculus
IEEE Transactions on Signal Processing
Journal of Signal Processing Systems
Order Selection of the Linear Mixing Model for Complex-Valued FMRI Data
Journal of Signal Processing Systems
A two-stage Independent Component Analysis-based method for blind detection in CDMA systems
Digital Signal Processing
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We introduce a framework based on Wirtinger calculus for nonlinear complex-valued signal processing such that all computations can be directly carried out in the complex domain. The two main approaches for performing independent component analysis, maximum likelihood, and maximization of non-Gaussianity-which are intimately related to each other-are studied using this framework. The main update rules for the two approaches are derived, their properties and density matching strategies are discussed along with numerical examples to highlight their relationships.