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ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

May 2014, Vol. 26, No. 5, Pages 907-919
(doi: 10.1162/NECO_a_00578)
© 2014 Massachusetts Institute of Technology
Prewhitening High-Dimensional fMRI Data Sets Without Eigendecomposition
Article PDF (297.45 KB)
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.