Simultaneous matrix diagonalization for structural brain networks classification

Nikita Mokrov, Maxim Panov, Boris A. Gutman, Joshua I. Faskowitz, Neda Jahanshad, Paul M. Thompson

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

    Abstract

    This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer’s disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification.

    Original languageEnglish
    Title of host publicationComplex Networks and Their Applications VI - Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications)
    EditorsHocine Cherifi, Chantal Cherifi, Mirco Musolesi, Márton Karsai
    PublisherSpringer Verlag
    Pages1261-1270
    Number of pages10
    ISBN (Print)9783319721491
    DOIs
    Publication statusPublished - 2018
    Event6th International Conference on Complex Networks and Their Applications, Complex Networks 2017 - Lyon, France
    Duration: 29 Nov 20171 Dec 2017

    Publication series

    NameStudies in Computational Intelligence
    Volume689
    ISSN (Print)1860-949X

    Conference

    Conference6th International Conference on Complex Networks and Their Applications, Complex Networks 2017
    Country/TerritoryFrance
    CityLyon
    Period29/11/171/12/17

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