LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering

• URL: http://arxiv.org/abs/2507.20980v2
• Date: Fri, 01 Aug 2025 05:21:32 GMT
• Title: LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering
• Authors: Shide Du, Chunming Wu, Zihan Fang, Wendi Zhao, Yilin Wu, Changwei Wang, Shiping Wang,
• Abstract summary: LargeMvC-Net is a novel deep network architecture for anchor-based multi-view clustering.<n>The proposed model decomposes the anchor-based clustering process into three modules.<n>Experiments on several large-scale multi-view benchmarks show that LargeMvC-Net consistently outperforms state-of-the-art methods in terms of both effectiveness and scalability.
• Score: 13.805932688128053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep anchor-based multi-view clustering methods enhance the scalability of neural networks by utilizing representative anchors to reduce the computational complexity of large-scale clustering. Despite their scalability advantages, existing approaches often incorporate anchor structures in a heuristic or task-agnostic manner, either through post-hoc graph construction or as auxiliary components for message passing. Such designs overlook the core structural demands of anchor-based clustering, neglecting key optimization principles. To bridge this gap, we revisit the underlying optimization problem of large-scale anchor-based multi-view clustering and unfold its iterative solution into a novel deep network architecture, termed LargeMvC-Net. The proposed model decomposes the anchor-based clustering process into three modules: RepresentModule, NoiseModule, and AnchorModule, corresponding to representation learning, noise suppression, and anchor indicator estimation. Each module is derived by unfolding a step of the original optimization procedure into a dedicated network component, providing structural clarity and optimization traceability. In addition, an unsupervised reconstruction loss aligns each view with the anchor-induced latent space, encouraging consistent clustering structures across views. Extensive experiments on several large-scale multi-view benchmarks show that LargeMvC-Net consistently outperforms state-of-the-art methods in terms of both effectiveness and scalability.

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