Statistical Baselines from Random Matrix Theory

Marotesa Voultsidou; J. Michael Herrmann

doi:10.1007/978-3-540-88906-9_46

Statistical Baselines from Random Matrix Theory

Marotesa Voultsidou, J. Michael Herrmann

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

Abstract

Quantitative descriptors of intrinsic properties of imaging data can be obtained from the theory of random matrices (RMT). Based on theoretical results for standardized data, RMT offers a systematic approach to surrogate data which allows us to evaluate the significance of deviations from the random baseline. Considering exemplary fMRI data sets recorded at a visuo-motor task and rest, we show their distinguishability by RMT-based quantities and demonstrate that the degree of sparseness and of localization can be evaluated in a strict way, provided that the data are sufficiently well described by the pairwise cross-correlations.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2008
Subtitle of host publication	9th International Conference Daejeon, South Korea, November 2-5, 2008 Proceedings
Editors	Colin Fyfe, Dongsup Kim, Soo-Young Lee, Hujun Yin
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	362-369
Number of pages	8
ISBN (Electronic)	978-3-540-88906-9
ISBN (Print)	978-3-540-88905-2
DOIs	https://doi.org/10.1007/978-3-540-88906-9_46
Publication status	Published - 2008

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin Heidelberg
Volume	5326
ISSN (Print)	0302-9743

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10.1007/978-3-540-88906-9_46

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Voultsidou, M., & Herrmann, J. M. (2008). Statistical Baselines from Random Matrix Theory. In C. Fyfe, D. Kim, S.-Y. Lee, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2008: 9th International Conference Daejeon, South Korea, November 2-5, 2008 Proceedings (pp. 362-369). (Lecture Notes in Computer Science; Vol. 5326). Springer. https://doi.org/10.1007/978-3-540-88906-9_46

Voultsidou, Marotesa ; Herrmann, J. Michael. / Statistical Baselines from Random Matrix Theory. Intelligent Data Engineering and Automated Learning – IDEAL 2008: 9th International Conference Daejeon, South Korea, November 2-5, 2008 Proceedings. editor / Colin Fyfe ; Dongsup Kim ; Soo-Young Lee ; Hujun Yin. Berlin, Heidelberg : Springer, 2008. pp. 362-369 (Lecture Notes in Computer Science).

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abstract = "Quantitative descriptors of intrinsic properties of imaging data can be obtained from the theory of random matrices (RMT). Based on theoretical results for standardized data, RMT offers a systematic approach to surrogate data which allows us to evaluate the significance of deviations from the random baseline. Considering exemplary fMRI data sets recorded at a visuo-motor task and rest, we show their distinguishability by RMT-based quantities and demonstrate that the degree of sparseness and of localization can be evaluated in a strict way, provided that the data are sufficiently well described by the pairwise cross-correlations.",

author = "Marotesa Voultsidou and Herrmann, {J. Michael}",

year = "2008",

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language = "English",

isbn = "978-3-540-88905-2",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

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Voultsidou, M & Herrmann, JM 2008, Statistical Baselines from Random Matrix Theory. in C Fyfe, D Kim, S-Y Lee & H Yin (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2008: 9th International Conference Daejeon, South Korea, November 2-5, 2008 Proceedings. Lecture Notes in Computer Science, vol. 5326, Springer, Berlin, Heidelberg, pp. 362-369. https://doi.org/10.1007/978-3-540-88906-9_46

Statistical Baselines from Random Matrix Theory. / Voultsidou, Marotesa; Herrmann, J. Michael.
Intelligent Data Engineering and Automated Learning – IDEAL 2008: 9th International Conference Daejeon, South Korea, November 2-5, 2008 Proceedings. ed. / Colin Fyfe; Dongsup Kim; Soo-Young Lee; Hujun Yin. Berlin, Heidelberg: Springer, 2008. p. 362-369 (Lecture Notes in Computer Science; Vol. 5326).

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

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AB - Quantitative descriptors of intrinsic properties of imaging data can be obtained from the theory of random matrices (RMT). Based on theoretical results for standardized data, RMT offers a systematic approach to surrogate data which allows us to evaluate the significance of deviations from the random baseline. Considering exemplary fMRI data sets recorded at a visuo-motor task and rest, we show their distinguishability by RMT-based quantities and demonstrate that the degree of sparseness and of localization can be evaluated in a strict way, provided that the data are sufficiently well described by the pairwise cross-correlations.

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Voultsidou M, Herrmann JM. Statistical Baselines from Random Matrix Theory. In Fyfe C, Kim D, Lee SY, Yin H, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2008: 9th International Conference Daejeon, South Korea, November 2-5, 2008 Proceedings. Berlin, Heidelberg: Springer. 2008. p. 362-369. (Lecture Notes in Computer Science). doi: 10.1007/978-3-540-88906-9_46

Statistical Baselines from Random Matrix Theory

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