High-order contrasts for independent component analysis
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Stability of Source Separation Algorithms
Journal of VLSI Signal Processing Systems
Maximum Likelihood Unsupervised Source Separation in Gaussian Noise
Journal of VLSI Signal Processing Systems
Blind separation methods based on Pearson system and its extensions
Signal Processing
Separation of independent components from data mixed by several mixing matrices
Signal Processing - Signal processing with heavy-tailed models
Robust blind source separation by beta divergence
Neural Computation
Kernel independent component analysis
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A multiscale framework for blind separation of linearly mixed signals
The Journal of Machine Learning Research
A maximum likelihood approach to single-channel source separation
The Journal of Machine Learning Research
Adaptive blind separation with an unknown number of sources
Neural Computation
Blind source separation using block-coordinate relative Newton method
Signal Processing
The Journal of Machine Learning Research
A multiscale framework for blind separation of linearly mixed signals
The Journal of Machine Learning Research
A maximum likelihood approach to single-channel source separation
The Journal of Machine Learning Research
Complex Infomax: Convergence and Approximation of Infomax with Complex Nonlinearities
Journal of VLSI Signal Processing Systems
Non-parametric approach to ICA using kernel density estimation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Kernel Methods for Measuring Independence
The Journal of Machine Learning Research
Fast kernel entropy estimation and optimization
Signal Processing - Special issue: Information theoretic signal processing
Some extensions of score matching
Computational Statistics & Data Analysis
Joint low-rank approximation for extracting non-Gaussian subspaces
Signal Processing
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
EURASIP Journal on Applied Signal Processing
Identification of discriminative features in the EEG
Intelligent Data Analysis
Blind separation of convolutive image mixtures
Neurocomputing
Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes
Neural Information Processing
Optimal approximation of signal priors
Neural Computation
Optimal Performance of Second-Order Multidimensional ICA
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
ICA with Sparse Connections: Revisited
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Blind separation of piecewise stationary non-Gaussian sources
Signal Processing
Fast approximate joint diagonalization incorporating weight matrices
IEEE Transactions on Signal Processing
Blind source separation based on self-organizing neural network
Engineering Applications of Artificial Intelligence
Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields
IEEE Transactions on Image Processing
Unsupervised learning with stochastic gradient
Neurocomputing
IEEE Transactions on Neural Networks
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Speed and accuracy enhancement of linear ICA techniques using rational nonlinear functions
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Blind audio source separation using sparsity based criterion for convolutive mixture case
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
An EM algorithm for independent component analysis using an AR-GGD source model
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Source extraction by maximizing the variance in the conditional distribution tails
IEEE Transactions on Signal Processing
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
The Journal of Machine Learning Research
Complex independent component analysis by entropy bound minimization
IEEE Transactions on Circuits and Systems Part I: Regular Papers
IEEE Transactions on Signal Processing
Independent component analysis by entropy bound minimization
IEEE Transactions on Signal Processing
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
Algorithms for complex ML ICA and their stability analysis using wirtinger calculus
IEEE Transactions on Signal Processing
Extensions of ICA for causality discovery in the hong kong stock market
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
An EM method for spatio-temporal blind source separation using an AR-MOG source model
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Markovian blind image separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Efficient separation of convolutive image mixtures
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind partial separation of instantaneous mixtures of sources
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Testing significance of mixing and demixing coefficients in ICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Nonlinear blind source separation applied to a simple bijective model
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models
The Journal of Machine Learning Research
| Hi-index | 35.70 |
We propose two methods for separating mixture of independent sources without any precise knowledge of their probability distribution. They are obtained by considering a maximum likelihood (ML) solution corresponding to some given distributions of the sources and relaxing this assumption afterward. The first method is specially adapted to temporally independent non-Gaussian sources and is based on the use of nonlinear separating functions. The second method is specially adapted to correlated sources with distinct spectra and is based on the use of linear separating filters. A theoretical analysis of the performance of the methods has been made. A simple procedure for optimally choosing the separating functions is proposed. Further, in the second method, a simple implementation based on the simultaneous diagonalization of two symmetric matrices is provided. Finally, some numerical and simulation results are given, illustrating the performance of the method and the good agreement between the experiments and the theory