Abstract:
Clustering is a classic data structure analysis method in machine learning and pattern recognition. The anchor-based graph has improved graph-based clustering accuracy recently. The progressive self-supervised clustering method with novel category discovery (PSSCNCD) uses three procedures to improve clustering performance. First, we propose a semisupervised framework with novel category discovery to guide label propagation processing, supported by the parameter-insensitive anchor-based graph from balanced K-means and hierarchical K-means (BKHK). Second, we design a novel representative point selected strategy based on our semisupervised framework to discover each representative point and endow pseudolabel progressively, where each pseudolabel hypothetically corresponds to a real category in each self-supervised label propagation. Third, after enough representative points are found, all sample labels will be predicted for terminal clustering. Our method outperforms others in several toy examples and benchmark data sets.
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