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 clinically relevant data of a novel treatment of Autism Spectrum Disorder.

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.

Title: Extracting Relationships by Multi-Domain Matching Summary Assuming that a corpus is compiled from many sources belonging to different to domains, of which only a strict subset of domains is suitable to learn how to do prediction in a target domain, this paper proposes a novel approach (called Multiple Domain Matching Network (MDMN)) that aims at learning which domains share strong statistical relationships, and which source domains are best at supporting to learn the target domain prediction tasks. While many approaches to multiple-domain adaptation aim to match the feature-space distribution of *every* source domain to that of the target space, this paper suggests to not only map the distribution between sources and target, but also *within* source domains. The latter allows for identifying subsets of source domains that share a strong statistical relationship. Strengths Paper provides a theoretical analysis that yields a tighter bound on the weighted multi-source discrepancy. Weaknesses Tighter bound on multi-source discrepancy depends on the assumption that source domains that are less relevant for the target domain have lower weights.

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.