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Fast Multiview Clustering by Optimal Graph Mining
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AI
DOI：10.1109/TNNLS.2023.3256066.png)
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摘要
En 中文
Multiview clustering (MVC) aims to exploit heterogeneous information from different sources and was extensively investigated in the past decade. However, far less attention has been paid to handling large-scale multiview data. In this brief, we fill this gap and propose a fast multiview clustering by an optimal graph mining model to handle large-scale data. We mine a consistent clustering structure from landmark-based graphs of different views, from which the optimal graph based on the one-hot encoding of cluster labels is recovered. Our model is parameter-free, so intractable hyperparameter tuning is avoided. An efficient algorithm of linear complexity to the number of samples is developed to solve the optimization problems. Extensive experiments on real-world datasets of various scales demonstrate the superiority of our proposal.
Keyword:
Clustering algorithms
Time complexity
Optimization
Clustering methods
Task analysis
Mathematical models
Learning systems
Anchor graph
graph cut
multiview clustering (MVC)
scalable clustering
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
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