  We present a hierarchical maximum-margin clustering method for unsupervised
data analysis. Our method extends beyond flat maximum-margin clustering, and
performs clustering recursively in a top-down manner. We propose an effective
greedy splitting criteria for selecting which cluster to split next, and employ
regularizers that enforce feature sharing/competition for capturing data
semantics. Experimental results obtained on four standard datasets show that
our method outperforms flat and hierarchical clustering baselines, while
forming clean and semantically meaningful cluster hierarchies.
