Clustering of noised and heterogeneous multi-view data with graph learning and projection decomposition

Complex systems in society and nature cannot be effectively modeled and represented by a single perspective, resulting in the so-called multi-view data, which provide an absolutely excellent chance to explore the fundamental mechanisms of systems. In comparison with single-view clustering, multi-view clustering simultaneously considers the intrinsic property within the same view and the relations across various views. Current methods for multi-view are criticized for the undesirable result because they fail to resolve the heterogeneity, consistency, and diversity of various views. To address these issues, we propose onsistency and iversity reserving with rojection