# Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

arXiv: https://arxiv.org/abs/2205.15075 · HTML: https://mlsanity.com/paper/2205.15075/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 7 of 7)

- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view](https://mlsanity.com/paper/2102.07943/) (2021)
- [Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity](https://mlsanity.com/paper/2203.11572/) (2022)
- [Multi-graph Fusion for Multi-view Spectral Clustering](https://mlsanity.com/paper/1909.06940/) (2019)
- [Projection onto the capped simplex](https://mlsanity.com/paper/1503.01002/) (2015)
- [A Unified Framework for Representation-based Subspace Clustering of Out-of-sample and Large-scale Data](https://mlsanity.com/paper/1309.6487/) (2013)
- [Multiple Partitions Aligned Clustering](https://mlsanity.com/paper/1909.06008/) (2019)

## Built on by (most cited first, 20 of 20)

- [Hard Sample Aware Network for Contrastive Deep Graph Clustering](https://mlsanity.com/paper/2212.08665/) (2022)
- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [Unpaired Multi-View Graph Clustering with Cross-View Structure Matching](https://mlsanity.com/paper/2307.03476/) (2023)
- [DealMVC: Dual Contrastive Calibration for Multi-view Clustering](https://mlsanity.com/paper/2308.09000/) (2023)
- [A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective](https://mlsanity.com/paper/2309.13989/) (2023)
- [Deep Multi-View Subspace Clustering with Anchor Graph](https://mlsanity.com/paper/2305.06939/) (2023)
- [Multiple Kernel Clustering with Dual Noise Minimization](https://mlsanity.com/paper/2207.06041/) (2022)
- [Scalable Incomplete Multi-View Clustering with Structure Alignment](https://mlsanity.com/paper/2308.16541/) (2023)
- [S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering](https://mlsanity.com/paper/2403.09107/) (2024)
- [MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment](https://mlsanity.com/paper/2408.09465/) (2024)
- [OpenViewer: Openness-Aware Multi-View Learning](https://mlsanity.com/paper/2412.12596/) (2024)
- [Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information](https://mlsanity.com/paper/2309.00474/) (2023)
- [Label Learning Method Based on Tensor Projection](https://mlsanity.com/paper/2402.16544/) (2024)
- [Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering](https://mlsanity.com/paper/2501.11898/) (2025)
- [One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering](https://mlsanity.com/paper/2401.15691/) (2024)
- [Probabilistically Aligned View-unaligned Clustering with Adaptive Template Selection](https://mlsanity.com/paper/2409.14882/) (2024)
- [Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering](https://mlsanity.com/paper/2504.04676/) (2025)
- [LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering](https://mlsanity.com/paper/2507.20980/) (2025)
- [One-step Multi-view Clustering With Adaptive Low-rank Anchor-graph Learning](https://mlsanity.com/paper/2509.14724/) (2025)
- [Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation](https://mlsanity.com/paper/2509.15955/) (2025)

## Abstract (arXiv)

Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied in large-scale applications, existing approaches do not pay sufficient attention to establishing correct correspondences between the anchor sets across views. To be specific, anchor graphs obtained from different views are not aligned column-wisely. Such an Anchor-Unaligned Problem (AUP) would cause inaccurate graph fusion and degrade the clustering performance. Under multi-view scenarios, generating correct correspondences could be extremely difficult since anchors are not consistent in feature dimensions. To solve this challenging issue, we propose the first study of the generalized and flexible anchor graph fusion framework termed Fast Multi-View Anchor-Correspondence Clustering (FMVACC). Specifically, we show how to find anchor correspondence with both feature and structure information, after which anchor graph fusion is performed column-wisely. Moreover, we theoretically show the connection between FMVACC and existing multi-view late fusion citeliu2018late and partial view-aligned clustering citehuang2020partially, which further demonstrates our generality. Extensive experiments on seven benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Moreover, the proposed alignment module also shows significant performance improvement applying to existing multi-view anchor graph competitors indicating the importance of anchor alignment. Our code is available at urlhttps://github.com/wangsiwei2010/NeurIPS22-FMVACC.