中文摘要：

针对标准SVM不能有效利用数据流形的局部信息以及对数据中的野值敏感的两点不足，提出一种基于自适应局部图嵌入加权罚SVM.算法在保持SVM优化框架不变的情况下，在目标函数中同时加入了对数据整体类间间隔最大化和数据局部流形分布的要求，优化了分类决策边界，简化了核化过程，同时在软间隔的样本惩罚系数中引入了数据的全局结构信息，增强了算法的鲁棒性.在人工、标准和图像数据集上的实验结果表明，所提出的方法是有效的.

英文摘要：

As a popular machine learning algorithm, the standard support vector machine（SVM） is faced with two problems：1） how to effectively use the local information of data manifold; 2） the classification hyperplane sensitive to the outliers in the data. Therefore, a learning algorithm called adaptive local graph embedding weighted-penalty support vector machine（ALGEWP-SVM） is proposed. On the condition of keeping the optimization framework of the standard SVM, the proposed algorithm joins the requirements of maximizing inter-class margin of the entire data and optimizing local distribution of the data manifold in the objective function, which optimizes the hyperplane of classification decision and simplifies the process of kernelization. Meanwhile, the proposed algorithm introduces the global structure information of data to automatically repress the influence of the outliers upon the hyperplane and improve the robustness of the algorithm. The results of the experiment on artificial, standard and image datasets show the effectiveness of the proposed algorithm.