New label propagation algorithm with pairwise constraints

Abstract

The label propagation algorithm is a well-known semi-supervised clustering method, which uses pre-given partial labels as constraints to predict the labels of unlabeled data. However, the algorithm has the following limitations: (1) it does not fully consider the misalignment between the pre-given labels and clustering labels, and (2) it only uses label information as clustering constraints. Real applications not only contain partial label information but pairwise constraints on a dataset. To overcome these deficiencies, a new version of the label propagation algorithm is proposed, which makes use of pairwise relations of labels as constraints to construct an optimization model for spreading labels. Experimental analysis was used to compare the proposed algorithm with 8 other semi-supervised clustering algorithms on 11 benchmark datasets. The experimental results demonstrated that the proposed algorithm is more effective than other algorithms.