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arXiv · 1611.08812

Kernel classification of connectomes based on earth mover's distance between graph spectra

Yulia Dodonova·Mikhail Belyaev·Anna Tkachev·Dmitry Petrov·Leonid Zhukov
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Abstract

In this paper, we tackle a problem of predicting phenotypes from structural connectomes. We propose that normalized Laplacian spectra can capture structural properties of brain networks, and hence graph spectral distributions are useful for a task of connectome-based classification. We introduce a kernel that is based on earth mover's distance (EMD) between spectral distributions of brain networks. We access performance of an SVM classifier with the proposed kernel for a task of classification of autism spectrum disorder versus typical development based on a publicly available dataset. Classification quality (area under the ROC-curve) obtained with the EMD-based kernel on spectral distributions is 0.71, which is higher than that based on simpler graph embedding methods.

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Yulia Dodonova, Mikhail Belyaev, Anna Tkachev, Dmitry Petrov, Leonid Zhukov. 2016-11-27. Kernel classification of connectomes based on earth mover's distance between graph spectra. https://arxiv.org/abs/1611.08812

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