chehreghani
Effective Acquisition Functions for Active Correlation Clustering
Aronsson, Linus, Chehreghani, Morteza Haghir
Correlation clustering is a powerful unsupervised learning paradigm that supports positive and negative similarities. In this paper, we assume the similarities are not known in advance. Instead, we employ active learning to iteratively query similarities in a cost-efficient way. In particular, we develop three effective acquisition functions to be used in this setting. One is based on the notion of inconsistency (i.e., when similarities violate the transitive property). The remaining two are based on information-theoretic quantities, i.e., entropy and information gain.
2402.03587
Country:
- North America > United States > California > San Francisco County > San Francisco (0.14)
- Asia > Afghanistan > Parwan Province > Charikar (0.04)
- North America > United States > Wisconsin > Dane County > Madison (0.04)
- (8 more...)
Technology: