A recursive feature retention method for semi-supervised feature selection

To deal with semi-supervised feature selection tasks, this paper presents a recursive feature retention (RFR) method based on a neighborhood discriminant index (NDI) method (a supervised feature selection method) and a forward iterative Laplacian score (FILS) method (an unsupervised method), where FILS is designed specially for RFR. The goal of RFR is to determine an optimal feature subset that has not only a high discriminant ability but also a strong ability to maintain the local structure of data. The discriminant ability of a feature is measured by NDI, and the ability of a feature to maintain the local structure of data is described by FILS. RFR compromises these two scores to give a balanced score for a feature. RFR iteratively selects a feature with the smallest balanced score and moves it into the current optimal feature subset. This paper also shows theoretical analysis to speed up iterations. Extensive experiments are conducted on toy and real-world data sets. Experimental results confirm that RFR can achieve a better performance compared with the state-of-the-art semi-supervised methods.