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Multiclass Total Variation Clustering
Xavier Bresson · Thomas Laurent · David Uminsky · James von Brecht

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor

Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation. While these algorithms perform well for bi-partitioning tasks, their recursive extensions yield unimpressive results for multiclass clustering tasks. This paper presents a general framework for multiclass total variation clustering that does not rely on recursion. The results greatly outperform previous total variation algorithms and compare well with state-of-the-art NMF approaches.

Author Information

Xavier Bresson (City University of Hong Kong)
Thomas Laurent (Loyola Marymount University)
David Uminsky (University of San Francisco)
James von Brecht (UCLA)

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