HS^2: Active Learning over Hypergraphs
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We propose a hypergraph-based active learning scheme which we term HS^2, HS^2 generalizes the previously reported algorithm S^2 originally proposed for graph-based active learning with pointwise queries [Dasarathy et al., COLT 2015]. Our HS^2 method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of HS^2 for the above described generalized settings. Both the theoretical and empirical results show that HS^2 requires a significantly fewer number of queries than S^2 when one uses S^2 over a graph obtained from the corresponding hypergraph via clique expansion.
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