Multi-view clustering via efficient representation learning with anchors

Multi-view spectral clustering has gained considerable attention due to its potential to enhance clustering performance. Although many methods have shown promising results, they often suffer from high time complexity and are not suitable for large-scale datasets. On the other hand, anchor-based methods are well-known for their efficiency. These methods typically learn the similarity relationship between instances and anchors and then convert it into the similarity relationship between instances, involving a considerable number of calculations. To address this issue, we propose a novel method called Multi-view clustering via Efficient Representation LearnIng with aNchors (MERLIN) in this paper. Instead of learning the instance–instance relationship, MERLIN approaches the clustering problem from the perspective of representation learning. Specifically, MERLIN selects the same anchors for different views and utilizes these anchors to learn a consensus representation that integrates information from all views. Additionally, MERLIN adaptively learns weights for different views to fully exploit the complementary information among multiple views. In comparison with seven state-of-the-art baseline methods across five datasets, MERLIN demonstrates both efficiency and effectiveness in handling multi-view datasets and is suitable for handling large-scale datasets.