Matrix analysis
Distance measures for signal processing and pattern recognition
Signal Processing
Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Blind Source Separation for Non-Stationary Mixing
Journal of VLSI Signal Processing Systems
Mixtures of Independent Component Analysers
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Estimation of Shape Parameter of GGD Function by Negentropy Matching
Neural Processing Letters
Journal of VLSI Signal Processing Systems
Channel selection and feature projection for cognitive load estimation using ambulatory EEG
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Mixed image separation using fastICA
SIP'08 Proceedings of the 7th WSEAS International Conference on Signal Processing
Single channel audio source separation
WSEAS Transactions on Signal Processing
FastICA algorithm for the separation of mixed images
WSEAS Transactions on Signal Processing
Complex nonconvex lp norm minimization for underdetermined source separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Bayesian independent component analysis with prior constraints: an application in biosignal analysis
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
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Independent component analysis (ICA) finds a linear transformation to variables that are maximally statistically independent. We examine ICA and algorithms for finding the best transformation from the point of view of maximizing the likelihood of the data. In particular, we discuss the way in which scaling of the unmixing matrix permits a "static" nonlinearity to adapt to various marginal densities. We demonstrate a new algorithm that uses generalized exponential functions to model the marginal densities and is able to separate densities with light tails. We characterize the manifold of decorrelating matrices and show that it lies along the ridges of high-likelihood unmixing matrices in the space of all unmixing matrices. We show how to find the optimum ICA matrix on the manifold of decorrelating matrices, and as an example we use the algorithm to find independent component basis vectors for an ensemble of portraits.