Flattening a Hierarchical Clustering through Active Learning

06/22/2019
by   Claudio Gentile, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/18/2020

Adaptive Region-Based Active Learning

We present a new active learning algorithm that adaptively partitions th...
research
01/21/2013

A Linear Time Active Learning Algorithm for Link Classification -- Full Version --

We present very efficient active learning algorithms for link classifica...
research
01/22/2013

Active Learning on Trees and Graphs

We investigate the problem of active learning on a given tree whose node...
research
03/03/2017

Active Learning for Cost-Sensitive Classification

We design an active learning algorithm for cost-sensitive multiclass cla...
research
06/06/2021

Neural Active Learning with Performance Guarantees

We investigate the problem of active learning in the streaming setting i...
research
09/10/2018

Learning Time Dependent Choice

We explore questions dealing with the learnability of models of choice o...
research
10/02/2022

Improved Algorithms for Neural Active Learning

We improve the theoretical and empirical performance of neural-network(N...

Please sign up or login with your details

Forgot password? Click here to reset