Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
A fast fixed-point algorithm for independent component analysis
Neural Computation
Natural gradient works efficiently in learning
Neural Computation
A Spurious Equilibria-free Learning Algorithm for the BlindSeparation of Non-zoer Skewness Signals
Neural Processing Letters
Extended ICA removes artifacts from electroencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Linear Feedforward Neural Network with Lateral Feedback Connections for Blind Source Separation
SPWHOS '97 Proceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics (SPW-HOS '97)
Natural Gradient Learning for Over-and Under-Complete Bases in ICA
Neural Computation
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Equivariant nonstationary source separation
Neural Networks
Adaptive blind separation with an unknown number of sources
Neural Computation
Information-theoretic assessment of multi-dimensional signals
Signal Processing - Special issue: Information theoretic signal processing
An information theoretic approach to a novel nonlinear independent component analysis paradigm
Signal Processing - Special issue: Information theoretic signal processing
Identification of discriminative features in the EEG
Intelligent Data Analysis
Separation capability of overcomplete ICA approaches
SIP'07 Proceedings of the 6th Conference on 6th WSEAS International Conference on Signal Processing - Volume 6
A constrained sequential EM algorithm for speech enhancement
Neural Networks
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Underdetermined blind source separation based on subspace representation
IEEE Transactions on Signal Processing
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Selective noise cancellation using independent component analysis
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Global and local preserving feature extraction for image categorization
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Fuzzy nonparametric DTI segmentation for robust cingulum-tract extraction
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
An offline independent component analysis algorithm for colored sources
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
An improved natural gradient algorithm for blind source separation
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Blind signal separation and identification of mixtures of images
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Estimating source kurtosis directly from observation data for ICA
Signal Processing
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Bayesian marginal statistics for speech enhancement using log Gabor wavelet
International Journal of Speech Technology
Global noise elimination from ELF band electromagnetic signals by independent component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Kernel independent component analysis for gene expression data clustering
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
An extended online Fast-ICA algorithm
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Hi-index | 0.00 |
This paper addresses an independent component analysis (ICA) learning algorithm with flexible nonlinearity, so named as flexible ICA, that is able to separate instantaneous mixtures of sub- and super-Gaussian source signals. In the framework of natural Riemannian gradient, we employ the parameterized generalized Gaussian density model for hypothesized source distributions. The nonlinear function in the flexible ICA algorithm is controlled by the Gaussian exponent according to the estimated kurtosis of demixing filter output. Computer simulation results and performance comparison with existing methods are presented.