Structured learning for unsupervised feature selection with high-order matrix factorization

Feature selection aims at searching the most discriminative and relevant features from high-dimensional data to improve the performance of certain learning tasks. Whereas, irrelevant or redundant features may increase the over-fitting risk of consequent learning algorithms. Structured learning of feature selection is to embed intrinsic structures of data, such as geometric structures and manifold structures, resulting in the improvement of...