Prewhitening high-dimensional fMRI data sets without eigendecomposition

Abd Krim Seghouane, Yousef Saad

Research output: Contribution to journalLetter

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)907-919
Number of pages13
JournalNeural computation
Volume26
Issue number5
DOIs
StatePublished - 2014

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