Adaptive Unsupervised Feature Selection With Structure Regularization.

Feature selection is one of the most important dimension reduction techniques for its efficiency and interpretation. Since practical data in large scale are usually collected without labels, and labeling these data are dramatically expensive and time-consuming, unsupervised feature selection has become a ubiquitous and challenging problem. Without label information, the fundamental problem of unsupervised feature selection lies in how to characterize the geometry structure of original feature space and produce a faithful feature subset, which preserves the intrinsic structure accurately. In this paper, we characterize the intrinsic local structure by an adaptive reconstruction graph and simultaneously consider its multiconnected-components (multicluster) structure by imposing a rank constraint on the corresponding Laplacian matrix. To achieve a desirable feature subset, we learn the optimal reconstruction graph and selective matrix simultaneously, instead of using a predetermined graph. We exploit an efficient alternative optimization algorithm to solve the proposed challenging problem, together with the theoretical analyses on its convergence and computational complexity. Finally, extensive experiments on clustering task are conducted over several benchmark data sets to verify the effectiveness and superiority of the proposed unsupervised feature selection algorithm.