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Proceedings Paper

Joint fMRI analysis and subject clustering using sparse dictionary learning
Author(s): Seung-Jun Kim; Krishna K. Dontaraju
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Paper Abstract

Multi-subject fMRI data analysis methods based on sparse dictionary learning are proposed. In addition to identifying the component spatial maps by exploiting the sparsity of the maps, clusters of the subjects are learned by postulating that the fMRI volumes admit a subspace clustering structure. Furthermore, in order to tune the associated hyper-parameters systematically, a cross-validation strategy is developed based on entry-wise sampling of the fMRI dataset. Efficient algorithms for solving the proposed constrained dictionary learning formulations are developed. Numerical tests performed on synthetic fMRI data show promising results and provides insights into the proposed technique.

Paper Details

Date Published: 24 August 2017
PDF: 12 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940F (24 August 2017); doi: 10.1117/12.2273914
Show Author Affiliations
Seung-Jun Kim, Univ. of Maryland, Baltimore County (United States)
Krishna K. Dontaraju, Univ. of Maryland, Baltimore County (United States)

Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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