Graph regularized locally linear embedding for unsupervised feature selection

作者：

Highlights：

• A novel unsupervised feature selection method is proposed which aims at preserving the local invariance of feature subspace by local linear embedding and manifold regularization simultaneously.

• An alternating algorithm based on alternative direction method of multiplier s with computational complexity analysis is designed to handle the resulting optimization problem.

• A variety of experiments on several publicly available real world datasets are conducted to demonstrate the effectiveness and superiority of the proposed method.

摘要

•A novel unsupervised feature selection method is proposed which aims at preserving the local invariance of feature subspace by local linear embedding and manifold regularization simultaneously.•An alternating algorithm based on alternative direction method of multiplier s with computational complexity analysis is designed to handle the resulting optimization problem.•A variety of experiments on several publicly available real world datasets are conducted to demonstrate the effectiveness and superiority of the proposed method.

论文关键词：Unsupervised feature selection,Local linear embedding,Graph Laplacian,Manifold regularization

论文评审过程：Received 17 July 2020, Revised 31 May 2021, Accepted 31 August 2021, Available online 2 September 2021, Version of Record 9 September 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108299