# Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering

arXiv: https://arxiv.org/abs/2412.02292 · HTML: https://mlsanity.com/paper/2412.02292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization](https://mlsanity.com/paper/1809.05998/) (2018)
- [Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View](https://mlsanity.com/paper/2008.09990/) (2020)
- [Multi-view Clustering via Deep Matrix Factorization and Partition Alignment](https://mlsanity.com/paper/2105.00277/) (2021)
- [Joint Multi-View Collaborative Clustering](https://mlsanity.com/paper/2311.12859/) (2023)

## Abstract (arXiv)

Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and rely on empirical hyperparameter selection. To address these issues, we introduce a novel Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering (DMFAW). Our method simultaneously incorporates feature selection and generates local partitions, enhancing clustering results. Notably, the features weights are controlled and adjusted by a parameter that is dynamically updated using Control Theory inspired mechanism, which not only improves the model's stability and adaptability to diverse datasets but also accelerates convergence. A late fusion approach is then proposed to align the weighted local partitions with the consensus partition. Finally, the optimization problem is solved via an alternating optimization algorithm with theoretically guaranteed convergence. Extensive experiments on benchmark datasets highlight that DMFAW outperforms state-of-the-art methods in terms of clustering performance.