Multi-view Unsupervised Feature Selection via Global and Local Kernelized Graph Learning

To cope with the impact of the nonlinear characteristics of real data on feature selection, we propose a new multi-view unsupervised feature selection method called Multi-view Unsupervised Feature Selection via Global and Local Kernelized Graph Learning (FSGLK). Our method leverages the self-representation property of samples to capture global structures through kernelized graph learning and utilizes the learned kernelized graph to construct high-order tensors for capturing high-order relationships between views. In addition, we employ a kernelized adaptive neighborhood strategy to enhance the model’s ability to capture the local structures of complex data. The constructed graph can more effectively capture both the local and global structures of multi-view data while eliminating redundant features in high-dimensional data. Symmetric non-negative matrix factorization is used to obtain low-dimensional representations, on which feature selection is performed. To flexibly control matrix row sparsity, the ${\ell }_{2,p}$-norm is introduced. Experimental evaluations on multiple benchmark datasets show that the proposed FSGLK method significantly outperforms existing methods in terms of clustering accuracy, consistency and information sharing.