Low-Rank Tensor-Based Two-Dimensional Projection Learning for Feature Extraction

Recently, Low-Rank Matrix (LRM)-based feature extraction methods have drawn increasing attention since they can extract robust features when the data are corrupted. However, these algorithms require a matrix-to-vector transformation to tackle Two-Dimensional (2D) images, through which the spatial structure residing in 2D images is ignored. To solve this problem, we propose a Low-Rank Tensor-based 2D Projection learning model (LRT-2DP) to extract features directly from 2D images as well as to reduce dimensionality. In essence, LRT-2DP embraces the global self-expressiveness property to denoise the corrupted data, from which a 2D projection basis is learned for robust feature extraction. The proposed LRT-2DP can be efficiently optimized with an alternative optimization scheme. Extensive experiments on image feature extraction have demonstrated the superiority of LRT-2DP compared to state-of-the-arts.