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Flattening a Hierarchical Clustering through Active Learning

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Abstract

We investigate active learning by pairwise similarity over the leaves of trees originating from hierarchical clustering procedures. In the realizable setting, we provide a full characterization of the number of queries needed to achieve perfect reconstruction of the tree cut. In the non-realizable setting, we rely on known important-sampling procedures to obtain regret and query complexity bounds. Our algorithms come with theoretical guarantees on the statistical error and, more importantly, lend themselves to linear-time implementations in the relevant parameters of the problem. We discuss such implementations, prove running time guarantees for them, and present preliminary experiments on real-world datasets showing the compelling practical performance of our algorithms as compared to both passive learning and simple active learning baselines.
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Dates and versions

hal-02376981 , version 1 (22-11-2019)

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  • HAL Id : hal-02376981 , version 1

Cite

Fabio Vitale, Anand Rajagopalan, Claudio Gentile. Flattening a Hierarchical Clustering through Active Learning. Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada. ⟨hal-02376981⟩
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