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Latent Maximum Margin Clustering
Guang-Tong Zhou · Tian Lan · Arash Vahdat · Greg Mori

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

We present a maximum margin framework that clusters data using latent variables. Using latent representations enables our framework to model unobserved information embedded in the data. We implement our idea by large margin learning, and develop an alternating descent algorithm to effectively solve the resultant non-convex optimization problem. We instantiate our latent maximum margin clustering framework with tag-based video clustering tasks, where each video is represented by a latent tag model describing the presence or absence of video tags. Experimental results obtained on three standard datasets show that the proposed method outperforms non-latent maximum margin clustering as well as conventional clustering approaches.

Author Information

Guang-Tong Zhou (Simon Fraser University)
Tian Lan (Simon Fraser University)
Arash Vahdat (Simon Fraser University)
Greg Mori (Borealis AI)

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