M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering
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Keywords: Graph Matching; Joint Optimization; Unsupervised Learning
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TL;DR: We propose learning-free solver M3C and unsupervised learning model UM3C to address the problem of mixture graph matching and clustering.
Abstract: Existing graph matching methods typically assume that there are similar structures between graphs and they are matchable. This work addresses a more realistic scenario where graphs exhibit diverse modes, requiring graph grouping before or along with matching, a task termed mixture graph matching and clustering. Specifically, we introduce Minorize-Maximization Matching and Clustering (M3C), a learning-free algorithm that guarantees theoretical convergence through the Minorize-Maximization framework and offers enhanced flexibility via relaxed clustering. Building on M3C, we further develop UM3C, an unsupervised model that incorporates novel edge-wise affinity learning and pseudo label selection. Extensive experimental results on public benchmarks demonstrate that our method outperforms state-of-the-art graph matching and mixture graph matching and clustering approaches in both accuracy and efficiency.
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Primary Area: representation learning for computer vision, audio, language, and other modalities
Submission Number: 2356
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