Flattening a Hierarchical Clustering through Active Learning

Vitale, Fabio; Rajagopalan, Anand; Gentile, Claudio

Type de document :

Communication dans un congrès avec actes

Titre :

Flattening a Hierarchical Clustering through Active Learning

Auteur(s) :

Vitale, Fabio [Auteur]
Machine Learning in Information Networks [MAGNET]
Rajagopalan, Anand [Auteur]
Google Inc
Gentile, Claudio [Auteur]
Google Inc

Titre de la manifestation scientifique :

Conference on Neural Information Processing Systems

Ville :

Vancouver

Pays :

Canada

Date de début de la manifestation scientifique :

2019-12-08

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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