This work proposes an extension to the maximum margin clustering (MMC) method that introduces latent variables. The motivation for adding latent variables is that they can model additional data semantics, resulting in better final clusters. The authors introduce a latent MMC (LMMC) objective, state how to optimize it, and then apply it to the task of video clustering. For this task, the latent variables are tag words, and the affinity of a video for a tag is given by a pre-trained binary tag detector. Experiments show that LMMC consistently, and sometimes substantially, beats several reasonable baselines.