Structure learning with consensus label information for multi-view unsupervised feature selection

Structure learning based feature selection has attracted increasing attention for selecting these features which can preserve the learned structures. However, existing methods fail to effectively explore the heterogeneous and homogeneous information from multiple views, which leads to the suboptimal results. To solve this problem, we propose Structure Learning with Consensus Label Information for Multi-View Feature Selection (SCMvFS). Noting the heterogeneity of views, the graph of each view should be a perturbation of the intrinsic graph yet the clustering structure are shared across views. In light of this, we generate a unique clustering indicator through the spectral analysis of multiple Laplacian graphs for the structure learning based feature selection. Therefore, SCMvFS considers both the graph heterogeneity and indicator consistency to effectively explore the heterogeneous and homogeneous information for facilitating the feature selection task. Further, we carefully design an efficient algorithm to solve the resulting optimization problem. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on seven benchmark datasets with respect to two indicators. In particular, SCMvFS achieves an ACC of 61.87 (55.94) on the Outdoor Scene (Yale) dataset, which is an up to 43% (15%) performance improvement compared with the latest structure learning based method TLR. The code and datasets are available at https://github.com/HdTgon/2023-ESWA-SCMvFS.

Automated summary

In this paper, we propose a structure learning method called SCMvFS for multi-view feature selection. The method effectively explores the heterogeneous and homogeneous information from multiple views to improve feature selection results.