American Journal of Mathematical and Management Sciences - Special issue: modern digital simulation methodology, II
Natural gradient works efficiently in learning
Neural Computation
Independent component analysis: theory and applications
Independent component analysis: theory and applications
The Fixed-Point Algorithm and Maximum Likelihood Estimation forIndependent Component Analysis
Neural Processing Letters
Novel blind source separation algorithms using cumulants
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Characteristic-function-based independent component analysis
Signal Processing - Special section: Security of data hiding technologies
Estimating the parameters of a generalized lambda distribution
Computational Statistics & Data Analysis
L-moments and TL-moments of the generalized lambda distribution
Computational Statistics & Data Analysis
Characterizing the generalized lambda distribution by L-moments
Computational Statistics & Data Analysis
Estimation of quantile mixtures via L-moments and trimmed L-moments
Computational Statistics & Data Analysis
| Hi-index | 0.00 |
We propose Blind Source Separation (BSS) techniques that are applicable to a wide class of source distributions that may be skewed or symmetric and may even have zero kurtosis. Skewed distributions are encountered in many important application areas such as communications and biomedical signal processing. The methods stem from maximum likelihood approach. Powerful parametric models based on the Extended Generalized Lambda Distribution (EGLD) and the Pearson system are employed in finding the score function. Model parameters are adaptively estimated using conventional moments or linear combinations of order statistics (L-moments). The developed methods are compared with the existing methods quantitatively. Simulation examples demonstrate the good performance of the proposed methods in the cases where the standard Independent Component Analysis (ICA) methods perform poorly.