Multi-label feature selection based on logistic regression and manifold learning

Yao Zhang, Yingcang Ma, Xiaofei Yang

Abstract: Like traditional single-label learning, multi-label learning is also faced with the problem of dimensional disaster. Feature selection is an effective technique for dimensionality reduction and learning efficiency improvement of high-dimensional data. This paper combined logistic regression, manifold learning, and sparse regularization to construct a joint framework for multi-label feature selection (LMFS). Firstly, the sparsity of the feature weight matrix is constrained by the L2, 1-norm. Secondly, the feature manifold and label manifold can constrain the feature weight matrix to fit the information of data and label better. An iterative updating algorithm is designed, and the convergence of the algorithm is proved. Finally, the LMFS algorithm is compared with DRMFS, SCLS, and other algorithms on eight classical multi-label data sets. The experimental results show the effectiveness of the LMFS algorithm.