Multi-view clustering via latent consistency multi-graph fusion

Multi-view clustering (MVC) algorithms are prevalent in machine learning tasks due to their superiority in mining complementary information from multi-view data. Despite their widespread use, existing MVC approaches ignore the presence of inconsistencies in the multi-view features during multi-view data fusion, which leads to suboptimal clustering performance. To address this challenge, the paper presents a novel approach called multi-view clustering via latent consistency multi-graph fusion. Specifically, a novel latent representation learning model is devised to learn a unified representation from multi-view features, simultaneously capturing the high-order relationships within the multi-view data. Additionally, to alleviate the adverse effects caused by the inconsistency multi-view feature, a consistent multi-graph fusion module is proposed to provide consistent constraints for latent representations. To be specific, the graphs that reflect the manifold structure for each view are fused to guide a consistent latent representation learning. After that, an alternating optimization algorithm is designed to update variables iteratively and alternatively, among which the fusion weights are assigned adaptively. Finally, comprehensive experiments conducted on seven widely recognized benchmark multi-view datasets affirm the efficacy of the proposed method, showcasing its superiority over state-of-the-art approaches.