Common subspace learning methods only utilize local or global structure in feature extraction, and cannot obtain the global optimal discriminative projection matrix. For this reason, this paper proposes a discriminative sparse subspace learning method based on the manifold regularization framework (DSSL-MR), which introduces the graph Laplacian matrix that reflects the intrinsic geometric structure of the sample as a penalty term. DSSL-MR simultaneously uses both sub-manifold and multi-manifold information of samples for obtaining optimal projection to enhance the discriminability of different classes in subspace. DSSL-MR uses the sparse property of the -norm to constrain the projection matrix, which can eliminate redundant features and select features that are significant for classification. It is a linear supervised method, which belongs to the Fisher discriminant analysis framework. Experimental results on multiple real-world datasets show that the algorithm is very effective in classification and has high recognition rates.

中文翻译：

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具有流形正则化的判别稀疏子空间学习

常见的子空间学习方法仅利用局部或全局结构进行特征提取，无法获得全局最优判别投影矩阵。为此，本文提出一种基于流形正则化框架的判别式稀疏子空间学习方法（DSSL-MR），引入反映样本内在几何结构的图拉普拉斯矩阵作为惩罚项。 DSSL-MR同时使用样本的子流形和多流形信息来获得最佳投影，以增强子空间中不同类别的区分度。 DSSL-MR利用-范数的稀疏特性来约束投影矩阵，可以消除冗余特征并选择对分类有意义的特征。它是一种线性监督方法，属于Fisher判别分析框架。在多个真实数据集上的实验结果表明，该算法分类非常有效，识别率较高。