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arXiv · 1502.01827

Hierarchical Maximum-Margin Clustering

Guang-Tong Zhou·Sung Ju Hwang·Mark Schmidt·Leonid Sigal·Greg Mori
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Abstract

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.

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Guang-Tong Zhou, Sung Ju Hwang, Mark Schmidt, Leonid Sigal, Greg Mori. 2015-02-06. Hierarchical Maximum-Margin Clustering. https://arxiv.org/abs/1502.01827

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