2020/01/18 by Marek Śmieja, Śmieja, Marek, Łukasz Struski +3 · 1 citation
Computer Science · #Advanced Clustering Algorithms Research #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.2001.06720
openalex publication_date 2020/01/18 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
In this paper, we introduce a neural network framework for semi-supervised\nclustering (SSC) with pairwise (must-link or cannot-link) constraints. In\ncontrast to existing approaches, we decompose SSC into two simpler\nclassification tasks/stages: the first stage uses a pair of Siamese neural\nnetworks to label the unlabeled pairs of points as must-link or cannot-link;\nthe second stage uses the fully pairwise-labeled dataset produced by the first\nstage in a supervised neural-network-based clustering method. The proposed\napproach, S3C2 (Semi-Supervised Siamese Classifiers for Clustering), is\nmotivated by the observation that binary classification (such as assigning\npairwise relations) is usually easier than multi-class clustering with partial\nsupervision. On the other hand, being classification-based, our method solves\nonly well-defined classification problems, rather than less well specified\nclustering tasks. Extensive experiments on various datasets demonstrate the\nhigh performance of the proposed method.\n