Scalable and parameter-free fusion graph learning for multi-view clustering

Multi-view clustering aims to capture the consistency and complementary information present in view-specific data to achieve clustering alignment. However, existing multi-view clustering methods often rely on different regularization terms to quantify the importance of various views, which inevitably introduces additional hyperparameters. It is challenging to fine-tune these additional parameters in real-world applications. Additionally, these methods suffer from high time complexity and impose substantial constraints when applied in large-scale scenarios. To address these limitations, we propose a parameter-free and time-efficient graph fusion method for multi-view clustering that can integrate view-specific graphs and directly generate clustering labels. Specifically, we introduce an anchor strategy and generate bipartite graphs on different views to enhance efficiency. Subsequently, we employ a self-weighted graph fusion strategy to merge the view-specific bipartite graphs. Finally, we propose a new solver to handle these problems, enabling the structured bipartite graphs to directly indicate clustering results. In contrast to previous clustering methods, our approach does not introduce any additional parameters and entirely relies on self-weighting for the fusion of view-specific graphs. As a result, our proposed method exhibits linear computational complexity to the data scale. Extensive experimental results on various benchmark datasets demonstrate the effectiveness and efficiency of our approach. Our code is available at https://github.com/DuannYu/MvSST.