Fused lasso for feature selection using structural information
作者：
Highlights：
• We propose a new feature selection method based on graph-based feature representations and the fused lasso framework.
• Our approach can accommodate structural relationship between pairs of samples through graph-based features.
• Our method can enhance the trade-off between the relevance of each feature and the redundancy between pairwise features.
• An iterative algorithm is developed to identify the most discriminative features.
• Experiments demonstrate that our proposed approach can outperform its competitors on benchmark datasets.
摘要
•We propose a new feature selection method based on graph-based feature representations and the fused lasso framework.•Our approach can accommodate structural relationship between pairs of samples through graph-based features.•Our method can enhance the trade-off between the relevance of each feature and the redundancy between pairwise features.•An iterative algorithm is developed to identify the most discriminative features.•Experiments demonstrate that our proposed approach can outperform its competitors on benchmark datasets.
论文关键词：Feature selection,Structural relationship,Fused lasso,Graph-based feature selection,Sparse learning,Correlated feature group
论文评审过程：Received 3 November 2020, Revised 26 April 2021, Accepted 18 May 2021, Available online 1 June 2021, Version of Record 16 June 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108058
