# Deep Embedded Multi-view Clustering with Collaborative Training

arXiv: https://arxiv.org/abs/2007.13067 · HTML: https://mlsanity.com/paper/2007.13067/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2020.

## Built on by (most cited first, 30 of 31)

- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [Multi-level Feature Learning for Contrastive Multi-view Clustering](https://mlsanity.com/paper/2106.11193/) (2021)
- [Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering](https://mlsanity.com/paper/2106.11232/) (2021)
- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (2021)
- [GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering](https://mlsanity.com/paper/2305.06799/) (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)
- [On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering](https://mlsanity.com/paper/2303.09877/) (2023)
- [Variational Graph Generator for Multi-View Graph Clustering](https://mlsanity.com/paper/2210.07011/) (2022)
- [MCoCo: Multi-level Consistency Collaborative Multi-view Clustering](https://mlsanity.com/paper/2302.13339/) (2023)
- [Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs](https://mlsanity.com/paper/2205.03803/) (2022)
- [Multi-view Semantic Consistency based Information Bottleneck for Clustering](https://mlsanity.com/paper/2303.00002/) (2023)
- [Contrastive Continual Multi-view Clustering with Filtered Structural Fusion](https://mlsanity.com/paper/2309.15135/) (2023)
- [A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRI](https://mlsanity.com/paper/2411.01859/) (2024)
- [Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information](https://mlsanity.com/paper/2309.00474/) (2023)
- [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)
- [Towards Learnable Anchor for Deep Multi-View Clustering](https://mlsanity.com/paper/2503.12427/) (2025)
- [Self-supervised Multi-view Clustering in Computer Vision: A Survey](https://mlsanity.com/paper/2309.09473/) (2023)
- [Self Supervised Correlation-based Permutations for Multi-View Clustering](https://mlsanity.com/paper/2402.16383/) (2024)
- [Adversarial Fair Multi-View Clustering](https://mlsanity.com/paper/2508.04071/) (2025)
- [DMVFC: Deep Learning Based Functionally Consistent Tractography Fiber Clustering Using Multimodal Diffusion MRI and Functional MRI](https://mlsanity.com/paper/2510.24770/) (2025)
- [Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints](https://mlsanity.com/paper/2206.04949/) (2022)
- [Towards Generalized Multi-stage Clustering: Multi-view Self-distillation](https://mlsanity.com/paper/2310.18890/) (2023)
- [Balanced Multi-view Clustering](https://mlsanity.com/paper/2501.02564/) (2025)
- [Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering](https://mlsanity.com/paper/2504.04676/) (2025)
- [Clustering Result Re-guided Incomplete Multi-view Spectral Clustering](https://mlsanity.com/paper/2510.09959/) (2025)
- [MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network](https://mlsanity.com/paper/2511.05554/) (2025)
- [MoEGCL: Mixture of Ego-Graphs Contrastive Representation Learning for Multi-View Clustering](https://mlsanity.com/paper/2511.05876/) (2025)

## Abstract (arXiv)

Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view clustering methods are either with high computation and space complexities, or lack of representation capability. To address these issues, we propose deep embedded multi-view clustering with collaborative training (DEMVC) in this paper. Firstly, the embedded representations of multiple views are learned individually by deep autoencoders. Then, both consensus and complementary of multiple views are taken into account and a novel collaborative training scheme is proposed. Concretely, the feature representations and cluster assignments of all views are learned collaboratively. A new consistency strategy for cluster centers initialization is further developed to improve the multi-view clustering performance with collaborative training. Experimental results on several popular multi-view datasets show that DEMVC achieves significant improvements over state-of-the-art methods.