Abstract
Feature selection facilitates intelligent information processing, and the unsupervised learning of feature selection has become important. In terms of unsupervised feature selection, the Laplacian score (LS) provides a powerful measurement and optimization method, and good performance has been achieved using the recent forward iterative Laplacian score (FILS) algorithm. However, there is still room for advancement. The aim of this paper is to improve the FILS algorithm, and thus, feature significance (SIG) is mainly introduced to develop a high-quality selection method, i.e., the incremental forward iterative Laplacian score (IFILS) algorithm. Based on the modified LS, the metric difference in the incremental feature process motivates SIG. Therefore, SIG offers a dynamic characterization by considering initial and terminal states, and it promotes the current FILS measurement on only the terminal state. Then, both the modified LS and integrated SIG acquire granulation nonmonotonicity and uncertainty, especially on incremental feature chains, and the corresponding verification is achieved by completing examples and experiments. Furthermore, a SIG-based incremental criterion of minimum selection is designed to choose optimization features, and thus, the IFILS algorithm is naturally formulated to implement unsupervised feature selection. Finally, an in-depth comparison of the IFILS algorithm with the FILS algorithm is achieved using data experiments on multiple datasets, including a nominal dataset of COVID-19 surveillance. As validated by the experimental results, the IFILS algorithm outperforms the FILS algorithm and achieves better classification performance.