Fast unsupervised feature selection with anchor graph and l2,1-norm regularization

Graph-based unsupervised feature selection has been proven to be effective in dealing with unlabeled and high-dimensional data. However, most existing methods face a number of challenges primarily due to their high computational complexity. In light of the ever-increasing size of data, these approaches tend to be inefficient in dealing with large-scale data sets. We propose a novel approach, called Fast Unsupervised Feature Selection (FUFS), to efficiently tackle this problem. Firstly, an anchor graph is constructed by means of a parameter-free adaptive neighbor assignment strategy. Meanwhile, an approximate nearest neighbor search technique is introduced to speed up the anchor graph construction. The a"" (2,1)-norm regularization is then performed to select more valuable features. Experiments on several large-scale data sets demonstrate the effectiveness and efficiency of the proposed method.