Sparse tensor dimensionality reduction with application to clustering of functional connectivity

Gaëtan Frusque; Julien Jung; Pierre Borgnat; Paulo Gonçalves

doi:10.1117/12.2529595

9 September 2019 Sparse tensor dimensionality reduction with application to clustering of functional connectivity

Gaëtan Frusque, Julien Jung, Pierre Borgnat, Paulo Gonçalves

Proceedings Volume 11138, Wavelets and Sparsity XVIII; 111380N (2019) https://doi.org/10.1117/12.2529595
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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.

Citation Download Citation

Gaëtan Frusque, Julien Jung, Pierre Borgnat, and Paulo Gonçalves "Sparse tensor dimensionality reduction with application to clustering of functional connectivity", Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111380N (9 September 2019); https://doi.org/10.1117/12.2529595

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