Memory Efficient PCA Methods for Large Group ICA
Abstract
Principal component analysis (PCA) is widely used for data reduction in group independent component analysis (ICA) of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory...
Paper Details
Title
Memory Efficient PCA Methods for Large Group ICA
Published Date
Feb 2, 2016
Journal
Volume
10
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