A classification-based approach to semi-supervised clustering with pairwise constraints. (July 2020)

• Record Type: Journal Article
• Authors: Śmieja, Marek; Struski, Łukasz; Figueiredo, Mário A.T.
• Is Part Of: Neural networks. Volume 127(2020)
• Page Start: 193
• Page End: 203
• Publication Date: 2020-07
• Subjects: Semi-supervised clustering -- Deep learning -- Neural networks -- Pairwise constraints -- Siamese neural networks; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.04.017
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, we introduce a neural network framework for semi-supervised clustering with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose semi-supervised clustering into two simpler classification tasks: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-link or cannot-link; the second stage uses the fully pairwise-labeled dataset produced by the first stage in a supervised neural-network-based clustering method. The proposed approach is motivated by the observation that binary classification (such as assigning pairwise relations) is usually easier than multi-class clustering with partial supervision. On the other hand, being classification-based, our method solves only well-defined classification problems, rather than less well specified clustering tasks. Extensive experiments on various datasets demonstrate the high performance of the proposed method.