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Graph Variational Multi-View Clustering
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AI
DOI：10.1109/TCSVT.2025.3571012.png)
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Abstract
En 中文
Multi-view clustering (MVC) aims to extract consensus information from multi-source data and has developed rapidly. Although generative model-based methods perform well by leveraging predefined priors, they often overlook inter-instance relationships, which are essential for high-quality clustering. To address this issue, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b>raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b>ariational <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b>ulti-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</b>iew <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b>lustering (GVMVC), which integrates graph information into the generative process. Specifically, we treat the original multi-view features and the graph information from each view as observed data to guide the learning of latent representations. The key principles of our approach are: 1) enhancing discriminative feature learning through graph integration; and 2) ensuring consistent multi-view learning via graph-based constraints. Extensive experiments show that GVMVC outperforms state-of-the-art methods across various datasets and metrics. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WenB777/GVMVC.git</uri>
Keywords:
Multi-view clustering
variational inference
Journal
IF:
11.1
Papers:
612
Citations:
3.1W