Machine learning algorithms | Computational statistics | Factor analysis

FastICA

FastICA is an efficient and popular algorithm for independent component analysis invented by Aapo Hyvärinen at Helsinki University of Technology. Like most ICA algorithms, FastICA seeks an orthogonal rotation of prewhitened data, through a fixed-point iteration scheme, that maximizes a measure of non-Gaussianity of the rotated components. Non-gaussianity serves as a proxy for statistical independence, which is a very strong condition and requires infinite data to verify. FastICA can also be alternatively derived as an approximative Newton iteration. (Wikipedia).

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From playlist Quickstart für Web und Tablet App

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From playlist Desmos LIVE

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From playlist Chapter 2 - Vectors

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From playlist Quickstart für Web und Tablet App

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From playlist Course 8: Fourier Analysis

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From playlist the Clojure language

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From playlist Vectors for Multivariable Calculus

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Covariance matrix | Independence (probability theory) | Iterative method | Expected value | RapidMiner | Function (mathematics) | FastICA | Independent component analysis | IT++ | Infomax