Kernel classification of connectomes based on earth mover's distance between graph spectra
arXiv preprint arXiv:1611.08812, 2016•arxiv.org
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 …
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 …
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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