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Seghouane AK, Saad Y. Prewhitening high-dimensional FMRI data sets without eigendecomposition. Neural Comput. 2014;26(5):907-19doi: 10.1162/NECO_a_00578.
Seghouane, A. K., & Saad, Y. (2014). Prewhitening high-dimensional FMRI data sets without eigendecomposition. Neural computation, 26(5), 907-19. https://doi.org/10.1162/NECO_a_00578
Seghouane, Abd-Krim, and Saad, Yousef. "Prewhitening high-dimensional FMRI data sets without eigendecomposition." Neural computation vol. 26,5 (2014): 907-19. doi: https://doi.org/10.1162/NECO_a_00578
Seghouane AK, Saad Y. Prewhitening high-dimensional FMRI data sets without eigendecomposition. Neural Comput. 2014 May;26(5):907-19. doi: 10.1162/NECO_a_00578. Epub 2014 Feb 20. PMID: 24555454.
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Neural Comput. 2014 May;26(5):907-19. doi: 10.1162/NECO_a_00578. Epub 2014 Feb 20.

Prewhitening high-dimensional FMRI data sets without eigendecomposition.

Neural computation

Abd-Krim Seghouane, Yousef Saad

Affiliations

  1. Department of Electrical and Electronic Engineering, Melbourne School of Engineering, University of Melbourne, Parkville, 3010, Australia [email protected].

PMID: 24555454 DOI: 10.1162/NECO_a_00578

Abstract

This letter proposes an algorithm for linear whitening that minimizes the mean squared error between the original and whitened data without using the truncated eigendecomposition (ED) of the covariance matrix of the original data. This algorithm uses Lanczos vectors to accurately approximate the major eigenvectors and eigenvalues of the covariance matrix of the original data. The major advantage of the proposed whitening approach is its low computational cost when compared with that of the truncated ED. This gain comes without sacrificing accuracy, as illustrated with an experiment of whitening a high-dimensional fMRI data set.

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