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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).
Sample gallery - Accord.NET Machine Learning in C#
Independent component analysis for blind source separation. Download the application; ... Learning and recognition of mouse gestures using hidden Markov model-based classifiers and Hidden Conditional Random Fields. ... Face detection using the Face detection based in Haar-like rectangular features method often known as the Viola-Jones method....
Randomized Independent Component Analysis | DeepAI
Independent component analysis (ICA) is a well-established problem in unsupervised learning and signal processing, with numerous applications including blind source separation, face recognition, and stock price prediction. onsider the following scenario.A couple of speakers are located in a room. Each of them plays a different sound .
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