Local adaptive learning for semi-supervised feature selection with group sparsity

Feature selection is often an important tool for many machine learning and data mining tasks. By largely removing the irrelevant features and reducing the complexity of the data processing, feature selection can significantly improve the performance of subsequent classification or clustering tasks. As a result of the rapid development of social networking, large amounts of high-dimensional data have been generated. Due to the high cost of collecting sufficient labels, graph-based semi-supervised feature selection algorithms have attracted the most research interest; however, these approaches neglect the local sparsity of data. Accordingly, motivated by the merits of adaptive learning and sparse learning, we propose a novel feature selection method with a local adaptive loss function and a global sparsity constraint in this paper. Our method can operate more flexibly to model data with different distributions. Moreover, when both the local and global sparsity of data is considered, our method is more capable of selecting the most discriminating features. Experimental results on various real-world applications demonstrate the effectiveness of the proposed feature selection method compared to several state-of-the-art methods.