Semi-supervised sparse feature selection based on multi-view Laplacian regularization ?
Multi-view Laplacian sparse feature selection (MLSFS) algorithm is proposed. Multi-view learning is utilized to exploit the complementation of different views features. A effective iterative algorithm is introduced to optimize the objective function. The convergence of the algorithm is proven. Experiments demonstrate MLSFS has good performance of feature selection.
sparse feature selection laplacian regularization multi-view learning semi-supervised learning