# GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering

arXiv: https://arxiv.org/abs/2305.06799 · HTML: https://mlsanity.com/paper/2305.06799/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 11 May 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Multi-view Contrastive Graph Clustering](https://mlsanity.com/paper/2110.11842/) (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)
- [Reconsidering Representation Alignment for Multi-view Clustering](https://mlsanity.com/paper/2103.07738/) (2021)
- [Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder](https://mlsanity.com/paper/1802.02305/) (2018)
- [A Survey on Multi-View Clustering](https://mlsanity.com/paper/1712.06246/) (2017)
- [Deep Multimodal Subspace Clustering Networks](https://mlsanity.com/paper/1804.06498/) (2018)
- [Deep Embedded Multi-view Clustering with Collaborative Training](https://mlsanity.com/paper/2007.13067/) (2020)
- [Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity](https://mlsanity.com/paper/2203.11572/) (2022)
- [Structure-Preserving Graph Representation Learning](https://mlsanity.com/paper/2209.00793/) (2022)
- [k-Means Clustering Is Matrix Factorization](https://mlsanity.com/paper/1512.07548/) (2015)
- [Multi-view Subspace Clustering via Partition Fusion](https://mlsanity.com/paper/1912.01201/) (2019)

## Built on by (most cited first, 30 of 30)

- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Rethinking Multi-view Representation Learning via Distilled Disentangling](https://mlsanity.com/paper/2403.10897/) (2024)
- [Differentiable Information Bottleneck for Deterministic Multi-view Clustering](https://mlsanity.com/paper/2403.15681/) (2024)
- [Trusted Mamba Contrastive Network for Multi-View Clustering](https://mlsanity.com/paper/2412.16487/) (2024)
- [Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation](https://mlsanity.com/paper/2503.04151/) (2025)
- [Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent](https://mlsanity.com/paper/2310.07166/) (2023)
- [PROTOCOL: Partial Optimal Transport-enhanced Contrastive Learning for Imbalanced Multi-view Clustering](https://mlsanity.com/paper/2506.12408/) (2025)
- [Learning Disentangled Representations for Generalized Multi-view Clustering](https://mlsanity.com/paper/2605.15640/) (2026)
- [Self Supervised Correlation-based Permutations for Multi-View Clustering](https://mlsanity.com/paper/2402.16383/) (2024)
- [Deep Contrastive Multi-view Clustering under Semantic Feature Guidance](https://mlsanity.com/paper/2403.05768/) (2024)
- [Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering](https://mlsanity.com/paper/2407.17744/) (2024)
- [Generalizable and Robust Spectral Method for Multi-view Representation Learning](https://mlsanity.com/paper/2411.02138/) (2024)
- [Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence](https://mlsanity.com/paper/2509.16022/) (2025)
- [Straight-Path Flow Matching for Incomplete Multi-View Clustering](https://mlsanity.com/paper/2607.06281/) (2026)
- [Consistency Enhancement-Based Deep Multiview Clustering via Contrastive Learning](https://mlsanity.com/paper/2401.12648/) (2024)
- [One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering](https://mlsanity.com/paper/2401.15691/) (2024)
- [Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views](https://mlsanity.com/paper/2410.09484/) (2024)
- [Mask-informed Deep Contrastive Incomplete Multi-view Clustering](https://mlsanity.com/paper/2502.02234/) (2025)
- [Multi-view Clustering via Bi-level Decoupling and Consistency Learning](https://mlsanity.com/paper/2508.13499/) (2025)
- [Generative Diffusion Contrastive Network for Multi-View Clustering](https://mlsanity.com/paper/2509.09527/) (2025)
- [Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation](https://mlsanity.com/paper/2511.01304/) (2025)
- [MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network](https://mlsanity.com/paper/2511.05554/) (2025)
- [THCRL: Trusted Hierarchical Contrastive Representation Learning for Multi-View Clustering](https://mlsanity.com/paper/2512.00368/) (2025)
- [SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering](https://mlsanity.com/paper/2512.15396/) (2025)
- [Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clustering](https://mlsanity.com/paper/2512.21510/) (2025)
- [PanORama: Multiview Consistent Panoptic Segmentation in Operating Rooms](https://mlsanity.com/paper/2603.19920/) (2026)
- [Robust Multi-view Clustering against Imperfect Information](https://mlsanity.com/paper/2606.04343/) (2026)
- [Robust Multi-View Classification under Noisy Supervision via Global Anchor Consensus](https://mlsanity.com/paper/2607.18561/) (2026)
- [Learning Deep Modality-Shared Self-Expressiveness for Image Clustering with Textual Information](https://mlsanity.com/paper/2608.08418/) (2026)
- [Adversarial Consistency-Guided Representation Learning for Multi-view Clustering](https://mlsanity.com/paper/2609.35212/) (2026)

## Abstract (arXiv)

Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views via view-wise aggregation way, where they ignore structure relationship of all samples. In this paper, we propose a novel multi-view clustering network to address these problems, called Global and Cross-view Feature Aggregation for Multi-View Clustering (GCFAggMVC). Specifically, the consensus data presentation from multiple views is obtained via cross-sample and cross-view feature aggregation, which fully explores the complementary ofsimilar samples. Moreover, we align the consensus representation and the view-specific representation by the structure-guided contrastive learning module, which makes the view-specific representations from different samples with high structure relationship similar. The proposed module is a flexible multi-view data representation module, which can be also embedded to the incomplete multi-view data clustering task via plugging our module into other frameworks. Extensive experiments show that the proposed method achieves excellent performance in both complete multi-view data clustering tasks and incomplete multi-view data clustering tasks.