{"ID":2889715,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20980","arxiv_id":"2507.20980","title":"LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering","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.","short_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 m...","url_abs":"https://arxiv.org/abs/2507.20980","url_pdf":"https://arxiv.org/pdf/2507.20980v2","authors":"[\"Shide Du\",\"Chunming Wu\",\"Zihan Fang\",\"Wendi Zhao\",\"Yilin Wu\",\"Changwei Wang\",\"Shiping Wang\"]","published":"2025-07-28T16:43:11Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"stat.CO\",\"stat.ML\"]","methods":"[]","has_code":false}