Monotonic convergence of fixed-point algorithms for ICA

We re-examine a fixed-point algorithm proposed by Hyvarinen for independent component analysis, wherein local convergence is proved subject to an ideal signal model using a square invertible mixing matrix. Here, we derive step-size bounds which ensure monotonic convergence to a local extremum for an...

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Veröffentlicht in:	IEEE transactions on neural networks Jg. 14; H. 4; S. 943 - 949
Hauptverfasser:	Regalia, P.A., Kofidis, E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.07.2003 Institute of Electrical and Electronics Engineers
Schlagworte:	Algorithms Appeals Background noise Convergence Engineering Sciences Fixed points (mathematics) Image restoration Independent component analysis Information processing Initial conditions Mathematical models Neural networks Probability density function Random variables Sensors Signal analysis Signal and Image processing Signal restoration Vectors Non-Gaussian signals Fixed-point algorithms Monotonic convergence Independent component analysis (ICA)
ISSN:	1045-9227
Online-Zugang:	Volltext
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