One-step Multi-view Clustering With Adaptive Low-rank Anchor-graph Learning

• URL: http://arxiv.org/abs/2509.14724v1
• Date: Thu, 18 Sep 2025 08:17:52 GMT
• Title: One-step Multi-view Clustering With Adaptive Low-rank Anchor-graph Learning
• Authors: Zhiyuan Xue, Ben Yang, Xuetao Zhang, Fei Wang, Zhiping Lin,
• Abstract summary: We develop a novel one-step multi-view clustering method with adaptive low-rank anchor-graph learning (OMCAL)<n>To construct a high-quality CAG, OMCAL provides a nuclear norm-based adaptive CAG learning model against information redundancy and noise interference.<n>Various studies conducted on ordinary and large-scale datasets indicate that OMCAL outperforms existing state-of-the-art methods in terms of clustering effectiveness and efficiency.
• Score: 10.643345522653949
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In light of their capability to capture structural information while reducing computing complexity, anchor graph-based multi-view clustering (AGMC) methods have attracted considerable attention in large-scale clustering problems. Nevertheless, existing AGMC methods still face the following two issues: 1) They directly embedded diverse anchor graphs into a consensus anchor graph (CAG), and hence ignore redundant information and numerous noises contained in these anchor graphs, leading to a decrease in clustering effectiveness; 2) They drop effectiveness and efficiency due to independent post-processing to acquire clustering indicators. To overcome the aforementioned issues, we deliver a novel one-step multi-view clustering method with adaptive low-rank anchor-graph learning (OMCAL). To construct a high-quality CAG, OMCAL provides a nuclear norm-based adaptive CAG learning model against information redundancy and noise interference. Then, to boost clustering effectiveness and efficiency substantially, we incorporate category indicator acquisition and CAG learning into a unified framework. Numerous studies conducted on ordinary and large-scale datasets indicate that OMCAL outperforms existing state-of-the-art methods in terms of clustering effectiveness and efficiency.

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