Latent information-guided one-step multi-view fuzzy clustering based on cross-view anchor graph

Although graph-inspired clustering methods have achieved impressive success in the area of multi-view data analysis, current methods still face several challenges. First, classical graph construction approaches are computationally expensive in terms of both time and space for large-scale data. Second, fusing graphs from different views remains a challenge. Third, most existing methods require additional post-processing steps to generate the label assignment matrix. To address the above challenges, this paper proposes a novel multi-view clustering algorithm, called latent information-guided one-step multi-view fuzzy clustering based on cross-view anchor graph. Specifically, we introduce an efficient cross-view anchor graph learning approach to construct the similarity matrix of multi-view data and extract latent information from the graph, which reduces the computational complexity of graph learning and inspires the optimization of the consensus membership matrix. Following that, our one-step multi-view fuzzy clustering algorithm can directly generate the final clustering result in an effective and straightforward manner. Furthermore, during the optimization process, the proposed method balances consensus matrix learning and view-specified membership exploration via a self-tuned weighting mechanism. The comprehensive experimental analysis demonstrates the superiority of our approach over the state-of-the-art approaches.