Consensus cluster structure guided multi-view unsupervised feature selection

As the volume of high-dimensional multi-view data continues to grow, there has been a significant development in multi-view unsupervised feature selection methods, particularly those that perform graph learning and feature selection simultaneously. These methods typically begin by constructing a consensus graph, which is then utilized to ensure that the projected samples maintain the local structure of data. However, these methods require data from multiple views to preserve the same manifold structure, which goes against the reality that similarities may vary across different views. On the other hand, despite inconsistencies between heterogeneous features, multiple views share a unique cluster structure. Inspired by this, we propose consensus cluster structure guided multi-view unsupervised feature selection (CCSFS). Specifically, we generate multiple cluster structures and fuse them into a consensus structure to guide feature selection. The proposed method unifies subspace learning, cluster analysis, consensus learning and sparse feature selection into one optimization framework. By leveraging the inherent interactions between these four subtasks, CCSFS can finally select informative and discriminative features. An efficient algorithm is carefully designed to solve the optimization problem of the objective function. We conduct extensive clustering experiments on seven multi-view datasets to demonstrate that the proposed method outperforms some of the latest competitors.