Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Journal of Multivariate Analysis
Second Order Nonstationary Source Separation
Journal of VLSI Signal Processing Systems
Second-order blind source separation in the Fourier space of data
Signal Processing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind source separation via generalized eigenvalue decomposition
The Journal of Machine Learning Research
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind source-separation using second-order cyclostationarystatistics
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
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Exploiting the fact that one is dealing with time signals, it is possible to formulate certain blind source (or signal) separation tasks in terms of a simple generalized eigenvalue decomposition based on two matrices. Many of the techniques determine these two matrices using second-order statistics, e.g., variance, covariance, autocorrelation, etc. In this work, we present a second-order, covariance-based method to determine the independent components of a linear mixture of sources. This is accomplished without the use of a possible temporal variable on which the data may depend, i.e., we explicitly avoid the use of autocorrelations, time delay, etc. in our formulation. The latter makes it possible to apply the simple eigenvalue decomposition-based technique to general pattern recognition methods and as such to find possible independent components of generic point clouds.