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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Vydané v:	IEEE transactions on neural networks Ročník 14; číslo 4; s. 943 - 949
Hlavní autori:	Regalia, P.A., Kofidis, E.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.07.2003 Institute of Electrical and Electronics Engineers
Predmet:	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
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