ICA using spacings estimates of entropy
The Journal of Machine Learning Research
Blind separation of sources that have spatiotemporal variance dependencies
Signal Processing - Special issue on independent components analysis and beyond
ICA using spacings estimates of entropy
The Journal of Machine Learning Research
A unifying model for blind separation of independent sources
Signal Processing
Algorithms with high order convergence speed for blind source extraction
Computational Optimization and Applications
Letters: Nonlinear innovation to blind source separation
Neurocomputing
Letters: Gaussian moments for noisy unifying model
Neurocomputing
Nonlinear Innovation to Noisy Blind Source Separation Based on Gaussian Moments
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
A fixed-point algorithm for blind source separation with nonlinear autocorrelation
Journal of Computational and Applied Mathematics
Blind source separation with nonlinear autocorrelation and non-Gaussianity
Journal of Computational and Applied Mathematics
Fast nonlinear autocorrelation algorithm for source separation
Pattern Recognition
Blind source separation based on cumulants with time and frequency non-properties
IEEE Transactions on Audio, Speech, and Language Processing
An Improved Fast ICA Algorithm for IR Objects Recognition
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
A post nonlinear geometric algorithm for independent component analysis
Digital Signal Processing
Complexity Pursuit for Unifying Model
Neural Processing Letters
Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields
IEEE Transactions on Image Processing
Novel nonGaussianity measure based BSS algorithm for dependent signals
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Constrained ICA for the analysis of high stimulus rate auditory evoked potentials
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Blind separation of non-stationary images using Markov models
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Research of blind images separation algorithm based on Kernel space
ICNC'09 Proceedings of the 5th international conference on Natural computation
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
The Journal of Machine Learning Research
Blind Source Separation Using Quadratic form Innovation
Neural Processing Letters
Blind dependent sources separation method using wavelet
International Journal of Computer Applications in Technology
Generic blind source separation using second-order local statistics
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Hybrid linear and nonlinear complexity pursuit for blind source separation
Journal of Computational and Applied Mathematics
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Blind separation of source signals usually relies either on the nonGaussianity of the signals or on their linear autocorrelations. A third approach was introduced by Matsuoka et al. (1995), who showed that source separation can be performed by using the nonstationarity of the signals, in particular the nonstationarity of their variances. In this paper, we show how to interpret the nonstationarity due to a smoothly changing variance in terms of higher order cross-cumulants. This is based on the time-correlation of the squares (energies) of the signals and leads to a simple optimization criterion. Using this criterion, we construct a fixed-point algorithm that is computationally very efficient