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ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data

Gruber, Peter and Meyer-Bäse, Anke and Foo, Simon and Theis, Fabian J. (2009) ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data. Engineering Applications of Artificial Intelligence 22 (4-5), p. 1111.

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Abstract

In the last decades, functional magnetic resonance imaging (fMRI) has been introduced into clinical practice. As a consequence of this advanced noninvasive medical imaging technique, the analysis and visualization of medical image time-series data poses a new challenge to both research and medical application. But often, the model data for a regression or generalized linear model-based analysis ...

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Item Type:Article
Date:2009
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
ValueType
10.1016/j.engappai.2008.11.010DOI
Keywords:Genetic Algorithm; Kernel Method; ROC; Nonnegative matrix factorization; Sparseness; PCA
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Owner: Gertraud Kellers
Deposited On:01 Oct 2010 08:04
Last Modified:01 Oct 2010 08:04
Item ID:16865
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