Sparse tensor dimensionality reduction with application to clustering of functional connectivity

Gaëtan Frusque 1 Julien Jung 2 Pierre Borgnat 3 Paulo Gonçalves 1
1 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
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.
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Gaëtan Frusque, Julien Jung, Pierre Borgnat, Paulo Gonçalves. Sparse tensor dimensionality reduction with application to clustering of functional connectivity. Wavelets and Sparsity XVIII, Aug 2019, San Diego, United States. pp.22, ⟨10.1117/12.2529595⟩. ⟨hal-02399385⟩

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