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Sparse tensor dimensionality reduction with application to clustering of functional connectivity
Author(s): Gaëtan Frusque; Julien Jung; Pierre Borgnat; Paulo Gonçalves
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Paper Abstract

Functional connectivity (FC) is a graph-like data structure commonly used by neuroscientists to study the dynamic behaviour of the brain activity. However, these analyses rapidly become complex and time-consuming. In this work, we present complementary empirical results on two tensor decomposition previously proposed named modified High Order Orthogonal Iteration (mHOOI) and High Order sparse Singular Value Decomposition (HOsSVD). These decompositions associated to k-means were shown to be useful for the study of multi trial functional connectivity dataset.

Paper Details

Date Published: 9 September 2019
PDF: 17 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111380N (9 September 2019); doi: 10.1117/12.2529595
Show Author Affiliations
Gaëtan Frusque, Univ. Lyon, INRIA, CNRS, Ecole Normale Supérieure de Lyon, UCB Lyon 1, LIP (France)
Julien Jung, HCL, Neuro. Hosp., Functional Neurology and Epileptology Dept. & Lyon Neurosc. Res. Ctr. (France)
Pierre Borgnat, Univ. Lyon, Ecole Normale Supérieure de Lyon, UCB Lyon 1, CNRS, Lab. de Physique (France)
Paulo Gonçalves, Univ. Lyon, INRIA, CNRS, Ecole Normale Supérieure de Lyon, UCB Lyon 1, LIP (France)


Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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