Fusion-enhanced multi-label feature selection with sparse supplementation

Abstract

The exponential increase of multi-label data over various domains demands the development of effective feature selection methods. However, current sparse-learning-based feature selection methods that use LASSO-norm and $l_{2,1}$-norm fail to handle two crucial issues for multi-label data. Firstly, LASSO-based methods remove features with zero-weight values during the feature selection process, some of which may have a certain degree of classification ability. Secondly, $l_{2,1}$-norm-based methods may select redundant features that lead to inefficient classification results. To overcome these issues, we propose a novel sparse supplementation norm that combines inner product regularization and $l_{2,1}$-norm as a novel fusion norm. This innovative fusion norm is designed to enhance the sparsity of feature selection models by leveraging the inherent row-sparse property in the $l_{2,1}$-norm. Specifically, the inner product regularization norm can maintain features with potentially useful classification information, which may be discarded in traditional LASSO-based methods. At the same time, the inner product regularization norm can remove redundant features, which is introduced in traditional $l_{2,1}$-norm-based methods. By incorporating this fusion norm into the Sparse-supplementation Regularized multi-label Feature Selection (SRFS) model, our method mitigates feature omission and feature redundancy, ensuring more effective and efficient feature selection for multi-label classification tasks. The experimental results on various benchmark datasets validate the efficiency and effectiveness of our proposed SRFS model.