independent component analysis

Thursday, November 8, 2018 - 10:40am - 11:10am
David S. Matteson (Cornell University)
Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to identify biomarkers in neurological disorders including Autism spectrum disorder [1] and dementia [2]. However, current group ICA methods use a PCA step that may remove important information associated with low-variance non-Gaussian features. Linear non-Gaussian component analysis (LNGCA) enables dimension reduction and component estimation simultaneously in single-subject fMRI [3].
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