# Scalable Incomplete Multi-View Clustering with Structure Alignment

arXiv: https://arxiv.org/abs/2308.16541 · HTML: https://mlsanity.com/paper/2308.16541/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 90 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 29 of 29)

- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Deep Graph Clustering via Dual Correlation Reduction](https://mlsanity.com/paper/2112.14772/) (2021)
- [Multi-level Feature Learning for Contrastive Multi-view Clustering](https://mlsanity.com/paper/2106.11193/) (2021)
- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Hard Sample Aware Network for Contrastive Deep Graph Clustering](https://mlsanity.com/paper/2212.08665/) (2022)
- [Cluster-guided Contrastive Graph Clustering Network](https://mlsanity.com/paper/2301.01098/) (2023)
- [Simple Contrastive Graph Clustering](https://mlsanity.com/paper/2205.07865/) (2022)
- [SoftCLIP: Softer Cross-modal Alignment Makes CLIP Stronger](https://mlsanity.com/paper/2303.17561/) (2023)
- [Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion](https://mlsanity.com/paper/2006.08159/) (2020)
- [Doubly Aligned Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.02785/) (2019)
- [Dink-Net: Neural Clustering on Large Graphs](https://mlsanity.com/paper/2305.18405/) (2023)
- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [Deep Temporal Graph Clustering](https://mlsanity.com/paper/2305.10738/) (2023)
- [Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment](https://mlsanity.com/paper/2302.07491/) (2023)
- [One-Pass Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.00637/) (2019)
- [Spectral Perturbation Meets Incomplete Multi-view Data](https://mlsanity.com/paper/1906.00098/) (2019)
- [Multi-view Deep Subspace Clustering Networks](https://mlsanity.com/paper/1908.01978/) (2019)
- [Mixed Graph Contrastive Network for Semi-Supervised Node Classification](https://mlsanity.com/paper/2206.02796/) (2022)
- [Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences](https://mlsanity.com/paper/2205.15075/) (2022)
- [Auto-weighted Multi-view Clustering for Large-scale Data](https://mlsanity.com/paper/2303.01983/) (2023)
- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph](https://mlsanity.com/paper/2207.02846/) (2022)
- [MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion](https://mlsanity.com/paper/2210.08821/) (2022)
- [Multiple Kernel Clustering with Dual Noise Minimization](https://mlsanity.com/paper/2207.06041/) (2022)
- [One-step Multi-view Clustering with Diverse Representation](https://mlsanity.com/paper/2306.05437/) (2023)
- [Market2Dish: Health-aware Food Recommendation](https://mlsanity.com/paper/2012.06416/) (2020)
- [Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning](https://mlsanity.com/paper/2307.03591/) (2023)
- [Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction](https://mlsanity.com/paper/2306.11020/) (2023)

## Built on by (most cited first, 7 of 7)

- [Contrastive Continual Multi-view Clustering with Filtered Structural Fusion](https://mlsanity.com/paper/2309.15135/) (2023)
- [Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering](https://mlsanity.com/paper/2501.11898/) (2025)
- [Enhancing Multi-view Open-set Learning via Ambiguity Uncertainty Calibration and View-wise Debiasing](https://mlsanity.com/paper/2508.01227/) (2025)
- [DWCL: Dual-Weighted Contrastive Learning for Multi-View Clustering](https://mlsanity.com/paper/2411.17354/) (2024)
- [LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering](https://mlsanity.com/paper/2507.20980/) (2025)
- [SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering](https://mlsanity.com/paper/2512.15396/) (2025)
- [Dual-Learning based Penalized Multi-Align Clustering for Multi-View Incomplete and Disorderly Data](https://mlsanity.com/paper/2606.27984/) (2026)

## Abstract (arXiv)

The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples are usually partially available due to data corruption or sensor malfunction, which raises the research of incomplete multi-view clustering (IMVC). Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID). Such the AUP-ID would cause inaccurate graph fusion and degrades clustering performance. To tackle these issues, we propose a novel incomplete anchor graph learning framework termed Scalable Incomplete Multi-View Clustering with Structure Alignment (SIMVC-SA). Specially, we construct the view-specific anchor graph to capture the complementary information from different views. In order to solve the AUP-ID, we propose a novel structure alignment module to refine the cross-view anchor correspondence. Meanwhile, the anchor graph construction and alignment are jointly optimized in our unified framework to enhance clustering quality. Through anchor graph construction instead of full graphs, the time and space complexity of the proposed SIMVC-SA is proven to be linearly correlated with the number of samples. Extensive experiments on seven incomplete benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Our code is publicly available at https://github.com/wy1019/SIMVC-SA.