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(PDF) Face Recognition by Independent Component Analysis
Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons.
Independent component analysis - Wikipedia
In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that the subcomponents are non-Gaussian signals and that they are statistically independent from each other. ICA is a special case of blind source separation.A common example application is the "cocktail party problem ......
ICA for dummies – Arnaud Delorme
Independent Component Analysis is a signal processing method to separate independent sources linearly mixed in several sensors. For instance, when recording electroencephalograms (EEG) on the scalp, ICA can separate out artifacts embedded in the data (since they are usually independent of each other).
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Face recognition using independent component analysis and ...
Independent component analysis (ICA) (Bell and Sejnowski, 1995) is also a relatively recent technique which has been mainly applied to blind signal separation, though it has been successfully applied to the face recognition problem too. ICA is a feature extraction technique, while SVM are a type of classifiers.
Independent Component Analysis (ICA) - Statistics
Independent Component Analysis (ICA) Independent Component Analysis (.pdf) . Independent component analysis (ICA) is a computational method from statistics and signal processing which is a special case of blind source separation. ICA seeks to separate a multivariate signal into additive subcomponents supposing the mutual statistical independence of the non-Gaussian source signals.
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Ensemble learning for independent component analysis ...
It is well known that the applicability of independent component analysis (ICA) to high-dimensional pattern recognition tasks such as face recognition often suffers from two problems. One is the small sample size problem. The other is the choice of basis functions (or independent components). Both problems make ICA classifier unstable and biased....