Extracting relationships by multi-domain matching

Yitong Li, Michael Andrew Murias, Samantha Major, Geraldine Dawson, David E. Carlson

    Research output: Contribution to journalConference articlepeer-review

    6 Scopus citations


    In many biological and medical contexts, we construct a large labeled corpus by aggregating many sources to use in target prediction tasks. Unfortunately, many of the sources may be irrelevant to our target task, so ignoring the structure of the dataset is detrimental. This work proposes a novel approach, the Multiple Domain Matching Network (MDMN), to exploit this structure. MDMN embeds all data into a shared feature space while learning which domains share strong statistical relationships. These relationships are often insightful in their own right, and they allow domains to share strength without interference from irrelevant data. This methodology builds on existing distribution-matching approaches by assuming that source domains are varied and outcomes multi-factorial. Therefore, each domain should only match a relevant subset. Theoretical analysis shows that the proposed approach can have a tighter generalization bound than existing multiple-domain adaptation approaches. Empirically, we show that the proposed methodology handles higher numbers of source domains (up to 21 empirically), and provides state-of-the-art performance on image, text, and multi-channel time series classification, including clinical outcome data in an open label trial evaluating a novel treatment for Autism Spectrum Disorder.

    Original languageEnglish (US)
    Pages (from-to)6798-6809
    Number of pages12
    JournalAdvances in Neural Information Processing Systems
    StatePublished - 2018
    Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
    Duration: Dec 2 2018Dec 8 2018

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing

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