Non-negative multi-label feature selection with dynamic graph constraints

Feature selection can combat dimension disasters and improve the performance of classification algorithms, so multi-label feature selection is an essential part of multi-label learning and has attracted widespread attention. Many existing multi-label feature selection methods either do not consider the correlation between labels or directly use logical labels to guide the feature selection process, which leads to the loss of label information. This paper proposes a non-negative multi-label feature selection (NMDG) with dynamic graph constraints to address this issue. In the NMDG model, the original data space is projected into a low-dimensional manifold space by linear regression to construct the pseudo label matrix. The pseudo label matrix has the same topological structure as the original data by combining the non-negative constraints and the label graph matrix. Then, the robust low-dimensional space of the pseudo label matrix is used to construct the dynamic graph matrix, which is combined with the feature manifold to guide the learning of the feature weight matrix. Finally, we design an iterative algorithm based on alternating optimization to solve the proposed method and give convergence proof. Experimental results on ten real multi-label data sets compared with seven representative methods show the effectiveness of the proposed method.(c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a non-negative multi-label feature selection method with dynamic graph constraints to address the loss of label information. Experimental results demonstrate the effectiveness of the proposed method.