Research map / Graph learning / Graph algorithms and clustering
Anchor-based multi-view clustering
Scale multi-view clustering with anchor graphs and alignment.
31 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2017–2026) · an approach in Graph algorithms and clustering, keyword cluster “graph clustering · spectral clustering · clusters”
State History Benchmarks Tried Baselines Checklist Preprints Who Export
Where it stands
Few recent reports None of the tracked benchmarks has a report from 2025–26.
7 papers in 2023–24 and 14 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 3.6% to 7.0%.
Open questions the data points to
- Papers report MLAN on BBCSport (NMI) anywhere from 0.689 to 0.906: comparisons against it depend on whose number you take.
- Papers report BMVC on AWA (NMI) anywhere from 0.119 to 0.137: comparisons against it depend on whose number you take.
No longer reported by papers here: Reuters (ACC, last in 2024).
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2017 · Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours (AAAI 2017 · 569 citations)
- 2020 · Large-Scale Multi-View Subspace Clustering in Linear Time (AAAI 2020 · 532 citations)
- 2021 · Fast Multi-view Discrete Clustering with Anchor Graphs (AAAI 2021 · 77 citations)
- 2022 · Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors (AAAI 2022 · 206 citations)
- 2023 · Let the Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering (AAAI 2023 · 72 citations)
- 2024 · A Non-parametric Graph Clustering Framework for Multi-View Data (AAAI 2024 · 31 citations)
- 2025 · Anchor Learning with Potential Cluster Constraints for Multi-view Clustering (AAAI 2025 · 25 citations)
- 2026 · Efficient Tensorized Multi-View Anchor Graph Clustering with Affinity Propagation for Remote Sensing Data (AAAI 2026 · 3 citations)
Earliest papers here
- Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours (AAAI 2017 · 569 citations)
- Large-Scale Multi-View Subspace Clustering in Linear Time (AAAI 2020 · 532 citations)
- CGD: Multi-View Clustering via Cross-View Graph Diffusion (AAAI 2020 · 201 citations)
Benchmark records
The best number each year that beat every earlier one, among papers of this approach reporting their own method. Numbers are extracted by AI from each paper's text; check the paper before citing.
Reuters · ACC (higher is better)
4 papers, 2020–2024 · not reported since 2024
| Year | Best so far | Set by |
|---|---|---|
| 2020 | 0.589 | Large-Scale Multi-View Subspace Clustering in Linear Time (AAAI 2020 · 532 citations) |
| 2023 | 0.612 | Let the Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering (AAAI 2023 · 72 citations) |
| 2024 | 0.618 | Embedded Feature Selection on Graph-Based Multi-View Clustering (AAAI 2024 · 17 citations) |
What has been tried
22 papers here report 214 measured changes (a component added, removed or swapped, compared with a baseline): helped 82%, no clear effect 4%, hurt 12%, mixed 2%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| add view-unrelated anchors to view-related anchors | Calte101view3 · ACC | 33.96±1.50 -> 35.55±2.25 | Helped | A Non-parametric Graph Clustering Framework for Multi-View Data (AAAI 2024 · 31 citations) |
| Add shared latent clustering structure | MSRC · ACC | 0.7507 -> 0.8819 | Helped | Anchor Learning with Potential Cluster Constraints for Multi-view Clustering (AAAI 2025 · 25 citations) |
| Add tensor Schatten p-norm to EFSGMC | MSRC · clustering performance | Helped | Embedded Feature Selection on Graph-Based Multi-View Clustering (AAAI 2024 · 17 citations) | |
| Add adaptive similarity completion | NGs, p=0.1 · ACC | 90.33 -> 100 | Helped | Fast Incomplete Multi-view Clustering with Adaptive Similarity Completion and Reconstruction (AAAI 2025 · 9 citations) |
| Use k-means anchors instead of DAS anchors | Prokaryotic · ACC | over 15% | Helped | Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering (AAAI 2025 · 3 citations) |
| remove image features | T. of London, M. Rushmore, E. Island · mean position error | 22.09 -> 37.78 | Hurt | Learning to Filter Outlier Edges in Global SfM (CVPR 2025 · 0 citations) |
| add perturbation generation network and anchor learning loss | Yale · ACC | 66.02 -> 78.18 | Helped | Towards Learnable Anchor for Deep Multi-View Clustering (AAAI 2025) |
| use a unified framework instead of separating graph construction, anchor learning, and graph partition | ORL · ACC (%) | 70.60±0.20 -> 78.84±0.50 | Helped | Fast Incomplete Multi-view Clustering by Flexible Anchor Learning (ICML 2025) |
Baseline numbers that disagree
The same baseline on the same benchmark and metric, reported more than 5% apart by different papers here. Training budgets, data splits and reimplementations differ, so state which number you compare with.
| Baseline | Benchmark | Metric | Reported | Papers |
|---|---|---|---|---|
| MLAN | BBCSport | NMI | 0.689 – 0.906 | 2 papers, e.g. AAAI 2021 and AAAI 2020 |
| BMVC | AWA | NMI | 0.119 – 0.137 | 2 papers, e.g. AAAI 2023 and AAAI 2022 |
| FPMVS | Caltech101-all | ACC | 0.285 – 0.301 | 2 papers, e.g. AAAI 2022 and AAAI 2025 |
What reviewers will expect
Datasets and baselines named in the 7 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.
Datasets
- MNIST · 2 of 7 papers
- NGs · 2 of 7 papers
- CCV · 2 of 7 papers
- WebKB · 2 of 7 papers
Baselines
- IMVC-CBG · 3 of 7 papers
- LMVSC · 2 of 7 papers
- FIMVC-VIA · 2 of 7 papers
- SIMVC-SA · 2 of 7 papers
- sFSR-IMVC · 2 of 7 papers
- DAIMC · 2 of 7 papers
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 21 papers; from OpenAlex and the papers' affiliations).
- National University of Defense Technology · 2 papers
- Xidian University · 2 papers
- University of Macau · 2 papers
Use it
Download BibTeX of 30 key papersOpen the topic in the interactive map
Get alerts. Describe your idea in scime atlas and save it (free scime account): you get a weekly email when new preprints land close to it.
Related approaches
Same keyword cluster
- Neural and local graph clustering (81 papers)
Elsewhere in the topic
- Neural combinatorial optimization on graphs (159 papers)
- Graph comparison and random features (155 papers)
- Graph embedding and spectral sparsification (126 papers)
- Continuous DAG structure learning (102 papers)
- Graph distance and retrieval (79 papers)
- Deep learning for graph matching (42 papers)
- Optimization algorithms for graph matching (42 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).