Classification of structural brain networks based on information divergence of graph spectra

Yulia Dodonova, Sergey Korolev, Anna Tkachev, Dmitry Petrov, Leonid Zhukov, Mikhail Belyaev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

This paper aims to tackle the problem of brain network classification with machine learning algorithms using spectra of networks' matrices. Two approaches are discussed: first, linear and tree-based models are trained on the vectors of sorted eigenvalues of the adjacency matrix, the Laplacian matrix and the normalized Laplacian; next, SVM classifier is trained with kernels based on information divergence between the eigenvalue distributions. The latter approach gives promising results in the classification of autism spectrum disorder versus typical development and of the carriers versus noncarriers of an allele associated with the high risk of Alzheimer disease.

Original languageEnglish
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditorsKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781509007462
DOIs
Publication statusPublished - 8 Nov 2016
Externally publishedYes
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: 13 Sep 201616 Sep 2016

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2016-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Country/TerritoryItaly
CityVietri sul Mare, Salerno
Period13/09/1616/09/16

Keywords

  • binary classification
  • Graph spectra
  • kernels
  • structural brain networks

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