Local convergence analysis of FastICA

Hao Shen; Knut Hüper

doi:10.1007/11679363_111

Local convergence analysis of FastICA

Hao Shen^*, Knut Hüper

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

The FastICA algorithm can be considered as a selfmap on a manifold. It turns out that FastICA is a scalar shifted version of an algorithm recently proposed. We put these algorithms into a dynamical system framework. The local convergence properties are investigated subject to an ideal ICA model. The analysis is very similar to the well-known case in numerical linear algebra when studying power iterations versus Rayleigh quotient iteration.

Original language	English
Title of host publication	Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings
Pages	893-900
Number of pages	8
DOIs	https://doi.org/10.1007/11679363_111
Publication status	Published - 2006
Event	6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 - Charleston, SC, United States Duration: 5 Mar 2006 → 8 Mar 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3889 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006
Country/Territory	United States
City	Charleston, SC
Period	5/03/06 → 8/03/06

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10.1007/11679363_111

Cite this

Shen, H., & Hüper, K. (2006). Local convergence analysis of FastICA. In Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings (pp. 893-900). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3889 LNCS). https://doi.org/10.1007/11679363_111

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title = "Local convergence analysis of FastICA",

abstract = "The FastICA algorithm can be considered as a selfmap on a manifold. It turns out that FastICA is a scalar shifted version of an algorithm recently proposed. We put these algorithms into a dynamical system framework. The local convergence properties are investigated subject to an ideal ICA model. The analysis is very similar to the well-known case in numerical linear algebra when studying power iterations versus Rayleigh quotient iteration.",

author = "Hao Shen and Knut H{\"u}per",

year = "2006",

doi = "10.1007/11679363_111",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 ; Conference date: 05-03-2006 Through 08-03-2006",

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Shen, H & Hüper, K 2006, Local convergence analysis of FastICA. in Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3889 LNCS, pp. 893-900, 6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006, Charleston, SC, United States, 5/03/06. https://doi.org/10.1007/11679363_111

Local convergence analysis of FastICA. / Shen, Hao; Hüper, Knut.
Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings. 2006. p. 893-900 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3889 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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BT - Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings

T2 - 6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006

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Local convergence analysis of FastICA

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