Skip to yearly menu bar Skip to main content


Poster

Flattening a Hierarchical Clustering through Active Learning

Fabio Vitale · Anand Rajagopalan · Claudio Gentile

East Exhibition Hall B, C #5

Keywords: [ Algorithms ] [ Active Learning ] [ Learning Theory ] [ Algorithms -> Clustering; Algorithms -> Semi-Supervised Learning; Theory ]


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 {\em 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.

Live content is unavailable. Log in and register to view live content