The traditional manifold learning methods usually utilize the original observed data to directly define the intrinsic structure among data. Because the original samples often contain a deal of redundant information or it is corrupted by noises, it leads to the unreliability of the obtained intrinsic structure. In addition, the intrinsic structure learning and subspace learning are completely separated. For solving above problems, this paper presents a novel dimension reduction method termed discriminative sparse embedding (DSE) based on adaptive graph. By projecting the original samples into a low-dimensional subspace, DSE learns a sparse weight matrix, which can reduce the effects of redundant information and noises of the original data, and uncover essential structural relationship among the data. In DSE, the robust subspace is learned from the original data. Meanwhile, the intrinsic local structure and the optimal subspace can be simultaneously learned, in which they are mutually improved, and the accurate structure can be captured, and the optimal subspace can be obtained. We propose an alternative and iterative method to solve the DSE model. In order to evaluate the performance of DSE, it is compared with some state-of-the-art feature extraction algorithms. Various experiments show that our DSE is effective and feasible.

传统的流形学习方法通​​常利用原始的观测数据直接定义数据之间的内在结构。由于原始样本通常包含大量冗余信息，或者由于噪声而损坏，因此导致获得的固有结构不可靠。另外，固有结构学习和子空间学习是完全分开的。为了解决上述问题，本文提出了一种新的基于自适应图的降维方法：判别式稀疏嵌入（DSE）。通过将原始样本投影到低维子空间中，DSE学习了一个稀疏的权重矩阵，该矩阵可以减少原始信息的冗余信息和噪声的影响，并揭示数据之间的基本结构关系。在DSE中，从原始数据中学习鲁棒子空间。同时，可以同时学习本征局部结构和最优子空间，并相互改进，并获取准确的结构，从而获得最优子空间。我们提出了另一种迭代方法来求解DSE模型。为了评估DSE的性能，将其与一些最新的特征提取算法进行了比较。各种实验表明，我们的DSE是有效可行的。为了评估DSE的性能，将其与一些最新的特征提取算法进行了比较。各种实验表明，我们的DSE是有效可行的。为了评估DSE的性能，将其与一些最新的特征提取算法进行了比较。各种实验表明，我们的DSE是有效可行的。