Abstract:
Hypergraph learning teaches hypergraph structures. Due to its flexibility and ability to model complex data correlation, hypergraph learning has gained popularity. First, we review distance-based, representation-based, attribute-based, and network-based hypergraph generation literature. Transductive, inductive, hypergraph structure updating, and multi-modal hypergraph learning are then presented. We then present a tensor-based dynamic hypergraph representation and learning framework for high-order correlation in hypergraphs. We evaluate hypergraph generation and learning methods on object and action recognition, Microblog sentiment prediction, and clustering to determine their efficacy. THU-HyperG is our hypergraph learning development toolkit.
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