# Joint Multi-View Collaborative Clustering

arXiv: https://arxiv.org/abs/2311.12859 · HTML: https://mlsanity.com/paper/2311.12859/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment](https://mlsanity.com/paper/1705.00825/) (2017)
- [Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization](https://mlsanity.com/paper/2308.04778/) (2023)

## Built on by (most cited first, 1 of 1)

- [Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering](https://mlsanity.com/paper/2412.02292/) (2024)

## Abstract (arXiv)

Data is increasingly being collected from multiple sources and described by multiple views. These multi-view data provide richer information than traditional single-view data. Fusing the former for specific tasks is an essential component of multi-view clustering. Since the goal of multi-view clustering algorithms is to discover the common latent structure shared by multiple views, the majority of proposed solutions overlook the advantages of incorporating knowledge derived from horizontal collaboration between multi-view data and the final consensus. To fill this gap, we propose the Joint Multi-View Collaborative Clustering (JMVCC) solution, which involves the generation of basic partitions using Non-negative Matrix Factorization (NMF) and the horizontal collaboration principle, followed by the fusion of these local partitions using ensemble clustering. Furthermore, we propose a weighting method to reduce the risk of negative collaboration (i.e., views with low quality) during the generation and fusion of local partitions. The experimental results, which were obtained using a variety of data sets, demonstrate that JMVCC outperforms other multi-view clustering algorithms and is robust to noisy views.