Correlation Clustering with Active Learning of Pairwise Similarities
Abstract: Correlation clustering is a well-known unsupervised learning setting that deals with positive and negative pairwise similarities. In this paper, we study the case where the pairwise similarities are not given in advance and must be queried in a cost-efficient way. Thereby, we develop a generic active learning framework for this task that benefits from several advantages, e.g., flexibility in the type of feedback that a user/annotator can provide, adaptation to any correlation clustering algorithm and query strategy, and robustness to noise. In addition, we propose and analyze a number of novel query strategies suited to this setting. We demonstrate the effectiveness of our framework and the proposed query strategies via several experimental studies.
Submission Length: Long submission (more than 12 pages of main content)
Code: https://github.com/Linusaronsson/Active-Correlation-Clustering
Assigned Action Editor: ~Novi_Quadrianto1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 1720
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