Fast orthogonal locality-preserving projections for unsupervised feature selection

Graph-based sparsity learning is one of the most successful unsupervised feature selection methods that has been widely adopted in many real-world applications. However, traditional graph-based unsupervised feature selection methods have several drawbacks: (1) being time-consuming and unable to deal with large-scale problems; (2) having difficulty tuning the regularization parameter with the sparsity regularization term; and (3) being unable to find explicit solutions owing to the limitation of sparsity, that is, feature selection with the 2,1-norm pound constrained problem. Thus, this paper proposes OLPPFS, a method to preserve the local geometric structure within the feature subspace by imposing the 2,0norm pound constraint. First, the linear mapping capability of the proposed model is enhanced using localitypreserving projections (LPPs), whichpreserve the local and global geometric manifold structure of the data while enhancing the ability to reconstruct data. Second, the graph-embedding learning method can accelerate the construction of a sparsity affinity graph and describe the intrinsic structure of the dataset well. More importantly, we propose a method for solving a projection matrix with the 2,0-norm pound constrained, which can accurately select a explicit group of discriminative feature subsets. This method can yield a more accurate sparse projection matrix than the 2,1-norm pound. We also adopt FOLPPFS, an effective anchor-based strategy to further accelerate our model with two flexible options. Extensive experiments on eight datasets demonstrate that the proposed method is superior to the other methods and can preserve a better local geometric structure of the dataset with less time consumption.(c) 2023 Elsevier B.V. All rights reserved.