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Proceedings chapter
English

Recent advances in supervised learning for brain graph classification

Presented at Austin (TX, USA), 3-5 Dec. 2013
PublisherIEEE
Publication date2013
Abstract

Modelling brain networks as graphs has become a dominant approach in neuroimaging. Substantial recent efforts in this area has led to a large number of new methods for analysing such brain graphs. In this paper, we review recent methods for estimating brain graphs and highlight some recent advances in predictive modelling on graphs. We divide the existing methods into three main categories, namely machine learning approaches, statistical hypothesis testing approaches, and network science ap- proaches, and discuss techniques associated with each approach as well as links between the approaches. Graph-based methods have strong roots in pattern recognition, computer vision, social sciences, and statistical physics, and many methods developed for brain graphs are readily transferable to other fields. We thus foresee this methodological upsurge in brain graph analysis will have a wide impact on applications beyond neuroimaging in years to come.

Funding
  • European Commission - Modelling and Inference on brain Networks for Diagnosis [299500]
Citation (ISO format)
RICHIARDI, Jonas, NG, Bernard. Recent advances in supervised learning for brain graph classification. In: Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE. Austin (TX, USA). [s.l.] : IEEE, 2013. p. 907–910. doi: 10.1109/GlobalSIP.2013.6737039
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Proceedings chapter (Published version)
accessLevelRestricted
Identifiers
ISBN978-1-4799-0248-4
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