Work

Enhanced Similarity Matrix Learning for Multi-View Clustering

Year: 2025

Type: article

Abstract: Graph-based multi-view clustering is a fundamental analysis method that learns the similarity matrix of multi-view data. Despite its success, it has two main limitations: (1) complementary information is not fully utilized by directly combining graphs from different views; (2) existing multi-view clustering methods do not adequately address redundancy and noise in the data, significantly affecting performance. To address these issues, we propose the Enhanced Similarity Matrix Learning (ES-MVC) for multi-view clustering, which dynamically integrates global graphs from all views with local graphs from each view to create an improved similarity matrix. Specifically, the global graph captures cross-view consistency, while the local graph preserves view-specific geometric patterns. The balance between global and local graphs is controlled through an adaptive weighting strategy, where hyperparameters adjust the relative importance of each graph, effectively capturing complementary information. In this way, our method can learn the clustering structure that contains fully complementary information, leveraging both global and local graphs. Meanwhile, we utilize a robust similarity matrix initialization to reduce the negative effects caused by noisy data. For model optimization, we derive an effective optimization algorithm that converges quickly, typically requiring fewer than five iterations for most datasets. Extensive experimental results on diverse real-world datasets demonstrate the superiority of our method over state-of-the-art multi-view clustering methods. In our experiments on datasets such as MSRC-v1, Caltech101, and HW, our proposed method achieves superior clustering performance with average accuracy (ACC) values of 0.7643, 0.6097, and 0.9745, respectively, outperforming the most advanced multi-view clustering methods such as OMVFC-LICAG, which yield ACC values of 0.7284, 0.4512, and 0.8372 on the same datasets.

Source: Electronics

DOI: 10.3390/electronics14142845

Authors: Dongdong Zhang, Pusheng Wang, Qin Li

Institutions: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen Institute of Information Technology

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Natural Science Foundation of Guangdong Province