EURASIP Journal on Applied Signal Processing
Universal linear precoding for NBI-proof widely linear equalization in MC systems
EURASIP Journal on Wireless Communications and Networking - Multicarrier Systems
Complex-valued ICA based on a pair of generalized covariance matrices
Computational Statistics & Data Analysis
Complex ICA using generalized uncorrelating transform
Signal Processing
A Flexible Natural Gradient Approach to Blind Separation of Complex Signals
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Complex valued recurrent neural networks for noncircular complex signals
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants
IEEE Transactions on Signal Processing
A complex generalized Gaussian distribution: characterization, generation, and estimation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of dependent sources
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A robust complex FastICA algorithm using the huber M-estimator cost function
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Properness and widely linear processing of quaternion random vectors
IEEE Transactions on Information Theory
Augmented second-order statistics of quaternion random signals
Signal Processing
Joint data detection and channel estimation for fading unknown time-varying Doppler environments
IEEE Transactions on Communications
Complex blind source extraction from noisy mixtures using second-order statistics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Quaternion-valued stochastic gradient-based adaptive IIR filtering
IEEE Transactions on Signal Processing
Essential statistics and tools for complex random variables
IEEE Transactions on Signal Processing
Bounded component analysis of linear mixtures: a criterion of minimum convex perimeter
IEEE Transactions on Signal Processing
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
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
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
On the asymptotic distribution of GLR for impropriety of complex signals
Signal Processing
Separation theorem for independent subspace analysis and its consequences
Pattern Recognition
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
ICA over finite fields-Separability and algorithms
Signal Processing
Cramér-Rao bound for circular complex independent component analysis
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Testing blind separability of complex Gaussian mixtures
Signal Processing
| Hi-index | 754.92 |
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