Manifold learning with structured subspace for multi-label feature selection
作者：
Highlights：
• The manifold learning is introduced to avoid too rigid fitting manner between input feature space and corresponding label space.
• A latent subspace is constructed to captures the correlations among instances, which learns a more accurate geometry structure of data.
• The label correlations are exploited in manifold framework, which ensures the global and local structural consistency of labels.
• An efficient algorithm is summarized to solve the optimization problem of the proposed method.
• Experiments are conducted on various of datasets, and the results of multiple metrics demonstrate the effectiveness of the proposed algorithm.
摘要
•The manifold learning is introduced to avoid too rigid fitting manner between input feature space and corresponding label space.•A latent subspace is constructed to captures the correlations among instances, which learns a more accurate geometry structure of data.•The label correlations are exploited in manifold framework, which ensures the global and local structural consistency of labels.•An efficient algorithm is summarized to solve the optimization problem of the proposed method.•Experiments are conducted on various of datasets, and the results of multiple metrics demonstrate the effectiveness of the proposed algorithm.
论文关键词：Multi-label learning,Feature selection,Manifold learning,Structured subspace,Instance correlations,Label correlations
论文评审过程：Received 19 December 2020, Revised 10 June 2021, Accepted 6 July 2021, Available online 20 July 2021, Version of Record 28 July 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108169
