Multi-view multi-instance multi-label learning based on collaborative matrix factorization
Proceedings of the AAAI Conference on Artificial Intelligence, 2019•aaai.org
Abstract Multi-view Multi-instance Multi-label Learning (M3L) deals with complex objects
encompassing diverse instances, represented with different feature views, and annotated
with multiple labels. Existing M3L solutions only partially explore the inter or intra relations
between objects (or bags), instances, and labels, which can convey important contextual
information for M3L. As such, they may have a compromised performance.\
encompassing diverse instances, represented with different feature views, and annotated
with multiple labels. Existing M3L solutions only partially explore the inter or intra relations
between objects (or bags), instances, and labels, which can convey important contextual
information for M3L. As such, they may have a compromised performance.\
Abstract
Multi-view Multi-instance Multi-label Learning (M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance.\
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