Low-rank learning for feature selection in multi-label classification

Abstract

Considering several research areas such as data mining, pattern recognition, and machine learning, it is difficult to handle high-dimensional data sets because of time consumption, large memory, and inefficient algorithms. Therefore, the feature selection method is widely used to address these problems. Several conventional feature selection methods have selected features by calculating the relationship between features and labels using some measures such as a mutual information. However, it is difficult to calculate mutual information because it requires to joint probability from a high-dimensional data set. Therefore, only few features can be considered, and a global search cannot be used. Moreover, calculating the correlation among labels using mutual information is limited because of insufficient label sets. In this study, we propose a feature selection method for multi-label classification based on low-rank learning. Unlike conventional methods, which use restricted search space, the proposed method considers a low-rank space from the entire feature space provided and the relationship between features and labels simultaneously. Therefore, we design a regression-based objective function using a nuclear norm and propose an efficient algorithm based on the gradient descent method to solve the optimization problem. The proposed method shows a better performance than the existing feature selection methods. This is based on the results of the multi-label classification experiments with five data sets and five multi-label classification performances.