G.: Latent maximum margin clustering

We present a maximum margin framework that clusters data using latent vari-ables. Using latent representations enables our framework to model unobserved information embedded in the data. We implement our idea by large margin learn-ing, 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 conven-tional clustering approaches.