Manifold learning with structured subspace for multi-label feature selection

Yuling Fan, Jinghua Liu, Peizhong Liu, Yongzhao Du, Weiyao Lan, Shunxiang Wu

Abstract: 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.