Manifold-based constraint Laplacian score for multi-label feature selection.

Highlights • Using manifold learning to transform original logical label space to Euclidean label space. • The similarity between samples is constrained by the similarity of corresponding numerical labels. • The final selection criterion integrates the influence of both the supervision information and local properties of the data. Abstract In recent years, multi-label learning has been increasingly applied to various application areas. As an important pre-processing technique for multi-label learning, multi-label feature selection selects meaningful features to improve classification performance. In this paper, a feature selection method named manifold-based constraint Laplacian score (MCLS) is presented. In MCLS, manifold learning is used to transform logical label space to Euclidean label space, and the similarity between samples is constrained by the corresponding numerical labels. The final selection criterion integrates the influence of both the supervision information and local properties of the data. Experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]