Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Natural gradient works efficiently in learning
Neural Computation
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Expert Systems with Applications: An International Journal
A switchable scheme for ECG beat classification based on independent component analysis
Expert Systems with Applications: An International Journal
Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
A non-parametric approach for independent component analysis using kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Superefficiency in blind source separation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Face Recognition Using an Enhanced Independent Component Analysis Approach
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, a source adaptive algorithm for linear instantaneous independent component analysis is proposed. This new algorithm is based on solving the estimating equation through Newton's method where no learning rate is needed which makes the proposed algorithm very easy to use. To achieve the source adaptivity, the density functions as well as their first and second derivatives are estimated by modified kernel density method. Empirical comparisons with several popular ICA algorithms confirm the efficiency and accuracy of the proposed algorithm.