# Multi-view Clustering via Unified Multi-kernel Learning and Matrix Factorization

arXiv: https://arxiv.org/abs/2412.09065 · HTML: https://mlsanity.com/paper/2412.09065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity](https://mlsanity.com/paper/2203.11572/) (2022)
- [SimpleMKKM: Simple Multiple Kernel K-means](https://mlsanity.com/paper/2005.04975/) (2020)
- [Auto-weighted Multi-view Clustering for Large-scale Data](https://mlsanity.com/paper/2303.01983/) (2023)
- [Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph](https://mlsanity.com/paper/2207.02846/) (2022)
- [One-step Multi-view Clustering with Diverse Representation](https://mlsanity.com/paper/2306.05437/) (2023)
- [Multiple Kernel k-Means Clustering by Selecting Representative Kernels](https://mlsanity.com/paper/1811.00264/) (2018)
- [A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to Subspace Clustering](https://mlsanity.com/paper/1709.10323/) (2017)
- [Multi-View Fuzzy Clustering with Minimax Optimization for Effective Clustering of Data from Multiple Sources](https://mlsanity.com/paper/1608.07005/) (2016)
- [Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss](https://mlsanity.com/paper/2201.04726/) (2022)

## Abstract (arXiv)

Multi-view clustering has become increasingly important due to the multi-source character of real-world data. Among existing multi-view clustering methods, multi-kernel clustering and matrix factorization-based multi-view clustering have gained widespread attention as mainstream approaches. However, multi-kernel clustering tends to learn an optimal kernel and then perform eigenvalue decomposition on it, which leads to high computational complexity. Matrix factorization-based multi-view clustering methods impose orthogonal constraints on individual views. This overly emphasizes the accuracy of clustering structures within single views and restricts the learning of individual views. Based on this analysis, we propose a multi-view clustering method that integrates multi-kernel learning with matrix factorization. This approach combines the advantages of both multi-kernel learning and matrix factorization. It removes the orthogonal constraints on individual views and imposes orthogonal constraints on the consensus matrix, resulting in an accurate final clustering structure. Ultimately, the method is unified into a simple form of multi-kernel clustering, but avoids learning an optimal kernel, thus reducing the time complexity. Furthermore, we propose an efficient three-step optimization algorithm to achieve a locally optimal solution. Experiments on widely-used real-world datasets demonstrate the effectiveness of our proposed method.