{
  "id": "1611.08812",
  "version": "v1",
  "published": "2016-11-27T09:35:04.000Z",
  "updated": "2016-11-27T09:35:04.000Z",
  "title": "Kernel classification of connectomes based on earth mover's distance between graph spectra",
  "authors": [
    "Yulia Dodonova",
    "Mikhail Belyaev",
    "Anna Tkachev",
    "Dmitry Petrov",
    "Leonid Zhukov"
  ],
  "comment": "Presented at The MICCAI-BACON 16 Workshop (arXiv:1611.03363)",
  "categories": [
    "cs.CV",
    "cs.NE"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-11-27T09:35:04.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "earth movers distance",
      "kernel classification",
      "connectomes",
      "brain networks",
      "simpler graph embedding methods"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}