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Activity Number: 585 - Exploiting Latent Structure for Network Inference
Type: Invited
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #300575
Title: Overlapping Clustering Models, and One (Class) SVM to Bind Them All.
Author(s): Purnamrita Sarkar*
Companies: University of Texas, Austin
Keywords: clustering; blockmodels; Support Vector Machines; overlapping; degree corrected

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. This is joint work with Xueyu Mao and Deepayan Chakrabarti.

Authors who are presenting talks have a * after their name.

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