Correlation Clustering with Active Learning of Pairwise Similarities
Artikel i vetenskaplig tidskrift, 2024

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.

active learning

acquisition function

correlation clustering

active clustering

Författare

Linus Aronsson

Chalmers, Data- och informationsteknik, Data Science och AI

Göteborgs universitet

Morteza Haghir Chehreghani

Chalmers, Data- och informationsteknik, Data Science och AI

Göteborgs universitet

Transactions on Machine Learning Research

28358856 (eISSN)

Vol. 2024

Ämneskategorier (SSIF 2011)

Datavetenskap (datalogi)

Mer information

Senast uppdaterat

2025-03-21