A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints

• URL: http://arxiv.org/abs/2001.06720v1
• Date: Sat, 18 Jan 2020 20:13:07 GMT
• Title: A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints
• Authors: Marek \'Smieja, {\L}ukasz Struski, M\'ario A. T. Figueiredo
• Abstract summary: We introduce a network framework for semi-supervised clustering with pairwise constraints. In contrast to existing approaches, we decompose SSC into two simpler classification tasks/stages. The proposed approach, S3C2, is motivated by the observation that binary classification is usually easier than multi-class clustering.
• Score: 5.639904484784126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose SSC into two simpler classification tasks/stages: 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, S3C2 (Semi-Supervised Siamese Classifiers for Clustering), 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.

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