Progressive Self-Supervised Clustering With Novel Category Discovery

These days, clustering is one of the most classical themes to analyze data structures in machine learning and pattern recognition. Recently, the anchor-based graph has been widely adopted to promote the clustering accuracy of plentiful graph-based clustering techniques. In order to achieve more sati...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2022-10, Vol.52 (10), p.10393-10406
Main Authors: Wang, Jingyu, Ma, Zhenyu, Nie, Feiping, Li, Xuelong
Format: Article
Language:English
Subjects:
Anchor-based graph
Bipartite graph
Clustering
Clustering algorithms
Clustering methods
Data models
Data structures
Laplace equations
Machine learning
Matrix decomposition
Optimization
Pattern recognition
progressively selected strategy
Propagation
pseudolabel
representative points
self-supervised clustering
semisupervised framework
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Summary:These days, clustering is one of the most classical themes to analyze data structures in machine learning and pattern recognition. Recently, the anchor-based graph has been widely adopted to promote the clustering accuracy of plentiful graph-based clustering techniques. In order to achieve more satisfying clustering performance, we propose a novel clustering approach referred to as the progressive self-supervised clustering method with novel category discovery (PSSCNCD), which consists of three separate procedures specifically. First, we propose a new semisupervised framework with novel category discovery to guide label propagation processing, which is reinforced by the parameter-insensitive anchor-based graph obtained 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 every pseudolabel hypothetically corresponds to a real category in each self-supervised label propagation. Third, when sufficient representative points have been found, the labels of all samples will be finally predicted to obtain terminal clustering results. In addition, the experimental results on several toy examples and benchmark data sets comprehensively demonstrate that our method outperforms other clustering approaches.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2021.3069836