Sparse tensor dimensionality reduction with application to clustering of functional connectivity

被引：1

作者：

Frusque, Gaetan ^{[1
]}

Jung, Julien ^{[2
,3
]}

Borgnat, Pierre ^{[4
]}

Goncalves, Paulo ^{[1
]}

机构：

[1] Univ Lyon, UCB Lyon 1, INRIA, ENS Lyon,LIP,CNRS,UMR 5668, F-69342 Lyon, France

[2] CNRS, INSERM, Neuro Hosp, Funct Neurol & Epileptol Dept,HCL, Lyon, France

[3] CNRS, INSERM, Lyon Neurosc Res Cent, Lyon, France

[4] Univ Lyon, UCB Lyon 1, ENS Lyon, CNRS,Lab Phys, F-69342 Lyon, France

来源：

WAVELETS AND SPARSITY XVIII | 2019年 / 11138卷

关键词：

dynamic networks; graph decomposition; clustering; dimensionality reduction; sparsity; tensor decompositions; HOOI; functional connectivity; iEEG;

D O I：

10.1117/12.2529595

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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.

引用

页数：17

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