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

摘要：

传统的子空间学习算法包含投影学习和分类两个过程，但是这两个过程分离，且对离群点较敏感，可能导致算法无法获得整体最优解。为此，提出了一种基于局部保持投影的鲁棒稀疏子空间学习算法。该算法将特征学习和分类模型相结合，使学习得到的子空间特征更具有判别性；利用L2，1范数的行稀疏性质，剔除冗余特征，同时在算法模型中考虑数据样本的局部关系来提高对离群点的鲁棒性；最后采用交替迭代方法来求解该模型。在不同数据集上的实验结果表明该算法具有较好的识别效果。

关键词: 图像处理, 子空间学习, 特征提取