Hierarchical Maximum-Margin Clustering
This work addresses the need for more effective hierarchical clustering in unsupervised learning, though it appears incremental as it builds upon existing maximum-margin clustering methods.
The paper tackles the problem of unsupervised data analysis by introducing a hierarchical maximum-margin clustering method that recursively performs clustering in a top-down manner, and it shows experimental results outperforming flat and hierarchical baselines on four standard datasets.
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.