Unsupervised graph-based feature selection via subspace and pagerank centrality
作者：
Highlights：
• Features that discriminate classes are linked to provide an undirected graph.
• Features relationships are defined based on unsupervised subspace learning.
• PageRank is investigated to rank features according to their importance in graph.
• High dimension low sample data are used to assess and compare the proposed method.
• The proposed unsupervised graph based method outperforms competitive methods.
摘要
•Features that discriminate classes are linked to provide an undirected graph.•Features relationships are defined based on unsupervised subspace learning.•PageRank is investigated to rank features according to their importance in graph.•High dimension low sample data are used to assess and compare the proposed method.•The proposed unsupervised graph based method outperforms competitive methods.
论文关键词：Unsupervised feature selection,Graph centrality measure,Pagerank,Subspace learning,Projected densities,K-Nearest neighbors
论文评审过程：Received 18 February 2018, Revised 12 July 2018, Accepted 13 July 2018, Available online 25 July 2018, Version of Record 25 July 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.07.029
