# Shared Generative Latent Representation Learning for Multi-view Clustering

arXiv: https://arxiv.org/abs/1907.09747 · HTML: https://mlsanity.com/paper/1907.09747/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 17 of 17)

- [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)
- [Semantic Invariant Multi-view Clustering with Fully Incomplete Information](https://mlsanity.com/paper/2305.12743/) (2023)
- [On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering](https://mlsanity.com/paper/2303.09877/) (2023)
- [Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs](https://mlsanity.com/paper/2205.03803/) (2022)
- [Cross-Modal and Multimodal Data Analysis Based on Functional Mapping of Spectral Descriptors and Manifold Regularization](https://mlsanity.com/paper/2105.05631/) (2021)
- [Unsupervised Document and Template Clustering using Multimodal Embeddings](https://mlsanity.com/paper/2506.12116/) (2025)
- [ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering](https://mlsanity.com/paper/2203.00186/) (2022)
- [Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings](https://mlsanity.com/paper/2104.14945/) (2021)
- [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)
- [Double Graphs Regularized Multi-view Subspace Clustering](https://mlsanity.com/paper/2209.15143/) (2022)
- [Towards Generalized Multi-stage Clustering: Multi-view Self-distillation](https://mlsanity.com/paper/2310.18890/) (2023)
- [Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering](https://mlsanity.com/paper/2504.04676/) (2025)
- [Simple Yet Effective Selective Imputation for Incomplete Multi-view Clustering](https://mlsanity.com/paper/2512.10327/) (2025)
- [Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering](https://mlsanity.com/paper/2608.15757/) (2026)

## Abstract (arXiv)

Clustering multi-view data has been a fundamental research topic in the computer vision community. It has been shown that a better accuracy can be achieved by integrating information of all the views than just using one view individually. However, the existing methods often struggle with the issues of dealing with the large-scale datasets and the poor performance in reconstructing samples. This paper proposes a novel multi-view clustering method by learning a shared generative latent representation that obeys a mixture of Gaussian distributions. The motivation is based on the fact that the multi-view data share a common latent embedding despite the diversity among the views. Specifically, benefited from the success of the deep generative learning, the proposed model not only can extract the nonlinear features from the views, but render a powerful ability in capturing the correlations among all the views. The extensive experimental results, on several datasets with different scales, demonstrate that the proposed method outperforms the state-of-the-art methods under a range of performance criteria.