Sparse feature selection based on L2,1/2-matrix norm for web image annotation
Spare Feature Selection method based on l2,1/2-matrix norm is proposed. Shared subspace learning is exploited to consider the feature correlation. A effective algorithm for optimizing the objective function is introduced. The convergence of the algorithm is proven. Experiments demonstrate the method is suit for web image annotation.
sparse feature selection web image annotation shared subspace learning l2 1/2-matrix norm