# Multi-graph Fusion for Multi-view Spectral Clustering

arXiv: https://arxiv.org/abs/1909.06940 · HTML: https://mlsanity.com/paper/1909.06940/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 27 of 27)

- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences](https://mlsanity.com/paper/2205.15075/) (2022)
- [Contrastive Multi-view Hyperbolic Hierarchical Clustering](https://mlsanity.com/paper/2205.02618/) (2022)
- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Graph Sparsification via Mixture of Graphs](https://mlsanity.com/paper/2405.14260/) (2024)
- [Consistent Multiple Graph Embedding for Multi-View Clustering](https://mlsanity.com/paper/2105.04880/) (2021)
- [Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification](https://mlsanity.com/paper/2303.17117/) (2023)
- [A Comprehensive Survey on Spectral Clustering with Graph Structure Learning](https://mlsanity.com/paper/2501.13597/) (2025)
- [Bures-Wasserstein Means of Graphs](https://mlsanity.com/paper/2305.19738/) (2023)
- [Multi-View Class Incremental Learning](https://mlsanity.com/paper/2306.09675/) (2023)
- [Multi-view Semantic Consistency based Information Bottleneck for Clustering](https://mlsanity.com/paper/2303.00002/) (2023)
- [Semantically Consistent Multi-view Representation Learning](https://mlsanity.com/paper/2303.04366/) (2023)
- [ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach](https://mlsanity.com/paper/2311.07355/) (2023)
- [A unified framework based on graph consensus term for multi-view learning](https://mlsanity.com/paper/2105.11781/) (2021)
- [Multi-source Education Knowledge Graph Construction and Fusion for College Curricula](https://mlsanity.com/paper/2305.04567/) (2023)
- [Fine-grained Graph Learning for Multi-view Subspace Clustering](https://mlsanity.com/paper/2201.04604/) (2022)
- [Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology](https://mlsanity.com/paper/2207.09657/) (2022)
- [Self-supervised Multi-view Clustering in Computer Vision: A Survey](https://mlsanity.com/paper/2309.09473/) (2023)
- [Deep Contrastive Multi-view Clustering under Semantic Feature Guidance](https://mlsanity.com/paper/2403.05768/) (2024)
- [Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques](https://mlsanity.com/paper/2512.05169/) (2025)
- [Double Graphs Regularized Multi-view Subspace Clustering](https://mlsanity.com/paper/2209.15143/) (2022)
- [Tucker-O-Minus Decomposition for Multi-view Tensor Subspace Clustering](https://mlsanity.com/paper/2210.12638/) (2022)
- [PaVa: a novel Path-based Valley-seeking clustering algorithm](https://mlsanity.com/paper/2306.07503/) (2023)
- [TCGF: A unified tensorized consensus graph framework for multi-view representation learning](https://mlsanity.com/paper/2309.09987/) (2023)
- [Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering](https://mlsanity.com/paper/2403.18393/) (2024)
- [Multi-view clustering integrating anchor attribute and structural information](https://mlsanity.com/paper/2410.21711/) (2024)
- [Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions](https://mlsanity.com/paper/2502.00302/) (2025)

## Abstract (arXiv)

A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and demonstrated promising results recently. Despite progress, there are still two fundamental questions that stay unanswered to date. First, how to fuse different views into one graph. More often than not, the similarities between samples may be manifested differently by different views. Many existing algorithms either simply take the average of multiple views or just learn a common graph. These simple approaches fail to consider the flexible local manifold structures of all views. Hence, the rich heterogeneous information is not fully exploited. Second, how to learn the explicit cluster structure. Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately. Those unreliable graphs might lead to suboptimal clustering results. To fill these gaps, in this paper, we propose a novel multi-view spectral clustering model which performs graph fusion and spectral clustering simultaneously. The fusion graph approximates the original graph of each individual view but maintains an explicit cluster structure. Experiments on four widely used data sets confirm the superiority of the proposed method.