Simultaneous matrix diagonalization for structural brain networks classification

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

    Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

    Аннотация

    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.

    Язык оригиналаАнглийский
    Название основной публикацииComplex Networks and Their Applications VI - Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications)
    РедакторыHocine Cherifi, Chantal Cherifi, Mirco Musolesi, Márton Karsai
    ИздательSpringer Verlag
    Страницы1261-1270
    Число страниц10
    ISBN (печатное издание)9783319721491
    DOI
    СостояниеОпубликовано - 2018
    Событие6th International Conference on Complex Networks and Their Applications, Complex Networks 2017 - Lyon, Франция
    Продолжительность: 29 нояб. 20171 дек. 2017

    Серия публикаций

    НазваниеStudies in Computational Intelligence
    Том689
    ISSN (печатное издание)1860-949X

    Конференция

    Конференция6th International Conference on Complex Networks and Their Applications, Complex Networks 2017
    Страна/TерриторияФранция
    ГородLyon
    Период29/11/171/12/17

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