关键词: 图像处理, 子空间学习, 特征提取

Abstract:

The traditional subspace learning algorithms include two processes：projection learning and classification, but the two processes are separated, and the algorithm is sensitive to outliers, which may not get the global optimal solution. To address these problems, the robust sparse subspace learning based on local preserving projections is proposed. In this method, feature learning and classification model are combined to make the obtained subspace features more discriminative. By using the row sparsity property of L2，1 norm, redundant features are eliminated, and the local relationship of data samples is considered in the algorithm model to improve the robustness of outliers. Finally, the iterative method is used to solve the model. Experimental results on different datasets show the good recognition effect of the proposed method.

Key words: image processing, subspace learning, feature extraction