A hybridization of simulated annealing and local PCA for automatic component assignment within ICA

M. Böhm; K. Stadlthanner; E. W. Lang; A. M. Tomé; A. R. Teixeira; F. J. Theis; C. G. Puntonet

doi:10.1007/11494669_132

A hybridization of simulated annealing and local PCA for automatic component assignment within ICA

M. Böhm, K. Stadlthanner, E. W. Lang, A. M. Tomé, A. R. Teixeira, F. J. Theis, C. G. Puntonet

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

Independent component analysis (ICA) as well as blind source separation (BSS) often faces the problem of assigning the independent or uncorrelated components estimated with ICA or BSS techniques to underlying source signals, artifacts or noise contributions. In this work an automatic assignment tool is presented which uses a priori knowledge about the form of some of the signals to be extracted. The algorithm is applied to the problem of removing water artifacts from 2D NOESY NMR spectra. The algorithm uses local PCA to approximate the water artifact and defines a suitable cost function which is optimized using simulated annealing. The blind source separation of the water artifact from the remaining protein spectrum is done with the recently developed algorithm dAMUSE.

Original language	English
Pages (from-to)	1075-1082
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3512
DOIs	https://doi.org/10.1007/11494669_132
State	Published - 2005
Externally published	Yes
Event	8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems - Vilanova i la Geltru, Spain Duration: 8 Jun 2005 → 10 Jun 2005

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Böhm, M., Stadlthanner, K., Lang, E. W., Tomé, A. M., Teixeira, A. R., Theis, F. J., & Puntonet, C. G. (2005). A hybridization of simulated annealing and local PCA for automatic component assignment within ICA. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3512, 1075-1082. https://doi.org/10.1007/11494669_132

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title = "A hybridization of simulated annealing and local PCA for automatic component assignment within ICA",

abstract = "Independent component analysis (ICA) as well as blind source separation (BSS) often faces the problem of assigning the independent or uncorrelated components estimated with ICA or BSS techniques to underlying source signals, artifacts or noise contributions. In this work an automatic assignment tool is presented which uses a priori knowledge about the form of some of the signals to be extracted. The algorithm is applied to the problem of removing water artifacts from 2D NOESY NMR spectra. The algorithm uses local PCA to approximate the water artifact and defines a suitable cost function which is optimized using simulated annealing. The blind source separation of the water artifact from the remaining protein spectrum is done with the recently developed algorithm dAMUSE.",

author = "M. B{\"o}hm and K. Stadlthanner and Lang, {E. W.} and Tom{\'e}, {A. M.} and Teixeira, {A. R.} and Theis, {F. J.} and Puntonet, {C. G.}",

year = "2005",

doi = "10.1007/11494669_132",

language = "English",

volume = "3512",

pages = "1075--1082",

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

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

note = "8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems ; Conference date: 08-06-2005 Through 10-06-2005",

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Böhm, M, Stadlthanner, K, Lang, EW, Tomé, AM, Teixeira, AR, Theis, FJ & Puntonet, CG 2005, 'A hybridization of simulated annealing and local PCA for automatic component assignment within ICA', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3512, pp. 1075-1082. https://doi.org/10.1007/11494669_132

A hybridization of simulated annealing and local PCA for automatic component assignment within ICA. / Böhm, M.; Stadlthanner, K.; Lang, E. W. et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3512, 2005, p. 1075-1082.

Research output: Contribution to journal › Conference article › peer-review

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T1 - A hybridization of simulated annealing and local PCA for automatic component assignment within ICA

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AU - Stadlthanner, K.

AU - Lang, E. W.

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AU - Teixeira, A. R.

AU - Theis, F. J.

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AB - Independent component analysis (ICA) as well as blind source separation (BSS) often faces the problem of assigning the independent or uncorrelated components estimated with ICA or BSS techniques to underlying source signals, artifacts or noise contributions. In this work an automatic assignment tool is presented which uses a priori knowledge about the form of some of the signals to be extracted. The algorithm is applied to the problem of removing water artifacts from 2D NOESY NMR spectra. The algorithm uses local PCA to approximate the water artifact and defines a suitable cost function which is optimized using simulated annealing. The blind source separation of the water artifact from the remaining protein spectrum is done with the recently developed algorithm dAMUSE.

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M3 - Conference article

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VL - 3512

SP - 1075

EP - 1082

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems

Y2 - 8 June 2005 through 10 June 2005

ER -

A hybridization of simulated annealing and local PCA for automatic component assignment within ICA

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