# Adaptive Weighted Multi-View Clustering

arXiv: https://arxiv.org/abs/2110.13240 · HTML: https://mlsanity.com/paper/2110.13240/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 58 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Multi-view Low-rank Sparse Subspace Clustering](https://mlsanity.com/paper/1708.08732/) (2017)
- [Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency](https://mlsanity.com/paper/2008.10208/) (2020)
- [Bayesian Consensus Clustering](https://mlsanity.com/paper/1302.7280/) (2013)

## Built on by (most cited first, 3 of 3)

- [Unified Transfer Learning Models in High-Dimensional Linear Regression](https://mlsanity.com/paper/2307.00238/) (2023)
- [Global-Graph Guided and Local-Graph Weighted Contrastive Learning for Unified Clustering on Incomplete and Noise Multi-View Data](https://mlsanity.com/paper/2512.21516/) (2025)
- [Clusterability-Based Assessment of Potentially Noisy Views for Multi-View Clustering](https://mlsanity.com/paper/2604.18024/) (2026)

## Abstract (arXiv)

Learning multi-view data is an emerging problem in machine learning research, and nonnegative matrix factorization (NMF) is a popular dimensionality-reduction method for integrating information from multiple views. These views often provide not only consensus but also complementary information. However, most multi-view NMF algorithms assign equal weight to each view or tune the weight via line search empirically, which can be infeasible without any prior knowledge of the views or computationally expensive. In this paper, we propose a weighted multi-view NMF (WM-NMF) algorithm. In particular, we aim to address the critical technical gap, which is to learn both view-specific weight and observation-specific reconstruction weight to quantify each view's information content. The introduced weighting scheme can alleviate unnecessary views' adverse effects and enlarge the positive effects of the important views by assigning smaller and larger weights, respectively. Experimental results confirm the effectiveness and advantages of the proposed algorithm in terms of achieving better clustering performance and dealing with the noisy data compared to the existing algorithms.