Multi-label feature selection with shared common mode

Abstract

Multi-label feature selection plays an indispensable role in multi-label learning, which eliminates irrelevant and redundant features while retaining relevant features. Most of existing multi-label feature selection methods employ two strategies to construct feature selection models: extracting label correlations to guide feature selection process and maintaining the consistency between the feature matrix and the reduced low-dimensional feature matrix. However, the data information is described by two data matrices: the feature matrix and the label matrix. Previous methods devote attention to either of the two data matrices. To address this issue, we propose a novel feature selection method named Feature Selection considering Shared Common Mode between features and labels (SCMFS). First, we utilize Coupled Matrix Factorization (CMF) to extract the shared common mode between the feature matrix and the label matrix, considering the comprehensive data information in the two matrices. Additionally, Non-negative Matrix Factorization (NMF) is adopted to enhance the interpretability for feature selection. Extensive experiments are implemented on fifteen real-world benchmark data sets for multiple evaluation metrics, the experimental results demonstrate the classification superiority of the proposed method.