Multi-label feature selection via constraint mapping space regularization

Abstract

Multi-label feature selection, an essential means of data dimension reduction in multi-label learning, has become one of the research hotspots in the field of machine learning. Because the linear assumption of sample space and label space is not suitable in most cases, many scholars use pseudo-label space. However, the use of pseudo-label space will increase the number of model variables and may lead to the loss of sample or label information. A multi-label feature selection scheme based on constraint mapping space regularization is proposed to solve this problem. The model first maps the sample space to the label space through the use of linear mapping. Second, given that the sample cannot be perfectly mapped to the label space, the mapping space should be closest to the label space and still retain the space of the basic manifold structure of the sample space, so combining the Hilbert-Schmidt independence criterion with the sample manifold, basic properties of constraint mapping space. Finally, the proposed algorithm is compared with MRDM, SSFS, and other algorithms on multiple classical multi-label data sets; the results show that the proposed algorithm is effective on multiple indicators.

Keywords

• multi-label learning
• feature selection
• hilbert-schmidt independence criterion
• manifold learning
• linear mapping