Abstract
We introduce a novel fast algorithm for independent component analysis, which can be used for blind source separation and feature extraction. We show how a neural network learning rule can be transformed into a fixedpoint iteration, which provides an algorithm that is very simple, does not depend on any user-defined parameters, and is fast to converge to the most accurate solution allowed by the data. The algorithm finds, one at a time, all nongaussian independent components, regardless of their probability distributions. The computations can be performed in either batch mode or a semiadaptive manner. The convergence of the algorithm is rigorously proved, and the convergence speed is shown to be cubic. Some comparisons to gradient-based algorithms are made, showing that the new algorithm is usually 10 to 100 times faster, sometimes giving the solution in just a few iterations.
Keywords
AlgorithmIndependent component analysisConvergence (economics)ComputationSimple (philosophy)Computer scienceArtificial neural networkRamer–Douglas–Peucker algorithmFixed pointBlind signal separationComponent (thermodynamics)MathematicsArtificial intelligenceChannel (broadcasting)
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Publication Info
- Year
- 1997
- Type
- article
- Volume
- 9
- Issue
- 7
- Pages
- 1483-1492
- Citations
- 3376
- Access
- Closed
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Cite This
Aapo Hyvärinen,
Erkki Oja
(1997).
A Fast Fixed-Point Algorithm for Independent Component Analysis.
Neural Computation
, 9
(7)
, 1483-1492.
https://doi.org/10.1162/neco.1997.9.7.1483
Identifiers
- DOI
- 10.1162/neco.1997.9.7.1483