Constrained Low-Rank Learning Using Least Squares-Based Regularization
Constrained Low-Rank Learning Using Least Squares-Based Regularization
Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization. Most low-rank methods are incapable of capturing low-dimensional subspace for supervised learning tasks, e.g., classification and regression. This paper aims to learn both the discriminant low-rank …