Fundamentals of digital image processing
Fundamentals of digital image processing
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
Matrix computations (3rd ed.)
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis by general nonlinear Hebbian-like learning rules
Signal Processing - Special issue on neural networks
Globally convergent blind source separation based on a multiuser kurtosis maximization criterion
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
A matrix-pencil approach to blind separation of colorednonstationary signals
IEEE Transactions on Signal Processing
A least squares approach to blind beamforming
IEEE Transactions on Signal Processing
Independent component analysis and (simultaneous) third-ordertensor diagonalization
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
A generalization of joint-diagonalization criteria for sourceseparation
IEEE Transactions on Signal Processing
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Adaptive unsupervised extraction of one component of a linear mixture with a single neuron
IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Adaptive blind separation with an unknown number of sources
Neural Computation
A novel algorithm for two-dimensional frequency estimation
Signal Processing
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
Convolutive blind separation of non-white broadband signals based on a double-iteration method
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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In this paper, we present an efficient off-fine algorithm for sequentially extracting one independent component from simultaneously mixed data corrupted by the spatially colored noises. For this purpose, this paper develops a new criterion and its efficient search algorithm to achieve the extraction of an independent component. The algorithm is an efficient, off-line update approach, and can find the global optimal solution of the cost function defined in this paper. By the systematic multistage decomposition and multistage reconstruction, we can get all the independent components. This algorithm uses only the second-order statistics of the source signals and suited particularly to separate the temporally colored signals. The main advantage of this algorithm over the conventional jointly approximated diagonalization of eigenmatrices (JADE) is its ability to separate sources from the simultaneously mixed data with the spatially colored noises.