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

arXiv: https://arxiv.org/abs/2509.14724 · HTML: https://mlsanity.com/paper/2509.14724/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Pseudo-supervised Deep Subspace Clustering](https://mlsanity.com/paper/2104.03531/) (2021)
- [Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation](https://mlsanity.com/paper/1802.08397/) (2018)
- [Decoupled Contrastive Multi-View Clustering with High-Order Random Walks](https://mlsanity.com/paper/2308.11164/) (2023)
- [An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges](https://mlsanity.com/paper/1704.02223/) (2017)
- [Incomplete Multi-view Clustering via Prototype-based Imputation](https://mlsanity.com/paper/2301.11045/) (2023)
- [Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences](https://mlsanity.com/paper/2205.15075/) (2022)
- [Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities](https://mlsanity.com/paper/1810.12544/) (2018)
- [Multi-view MERA Subspace Clustering](https://mlsanity.com/paper/2305.09095/) (2023)
- [Scalable Multi-view Clustering with Graph Filtering](https://mlsanity.com/paper/2205.09228/) (2022)
- [Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent](https://mlsanity.com/paper/2310.07166/) (2023)

## Abstract (arXiv)

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.