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People belong to multiple communities, words belong to multiple topics, and books cover multiple genres; overlapping clusters are commonplace. Many existing overlapping clustering methods model each person (or word, or book) as a non-negative weighted combination of "exemplars" who belong solely to one community, with some small noise. Geometrically, each person is a point on a cone whose corners are these exemplars. This basic form encompasses the widely used Mixed Membership Stochastic Blockmodel of networks and its degree-corrected variants, as well as topic models such as LDA. We show that a simple one-class SVM yields provably consistent parameter inference for all such models, and scales to large datasets. Experimental results on several simulated and real datasets show our algorithm (called SVM-cone) is both accurate and scalable.
Author Information
Xueyu Mao (University of Texas at Austin)
Purnamrita Sarkar (UT Austin)
Deepayan Chakrabarti (UT Austin)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Poster: Overlapping Clustering Models, and One (class) SVM to Bind Them All »
Thu Dec 6th 03:45 -- 05:45 PM Room Room 517 AB
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