Structural information is very important for improving the classification performance of classifiers. In order to increase the generalization performance of support vector machine (SVM) directly, several kinds of structured SVMs have been proposed. These algorithms with structural information only simply embed the global structural information or the local within-class information into SVM model. Thus, they sometimes are not suitable for real-world problems. To overcome the drawbacks, we firstly propose a novel SVM with local structural information (LSI-SVM) in this paper. In the LSI-SVM, the K-nearest neighbor (KNN) method is adopted. Applying the KNN method, the farthest neighbors set intra-class and the nearest neighbors set inter-class of the overall samples are obtained. It is more reasonable to maximize the margin between the nearest neighbors and the farthest neighbors. Both the global and local data structures are added into the optimization problem, making the LSI-SVM can fully utilize the underlying structural information and yield better performance. Furthermore, for nonlinear classification, the reproducing kernel Hilbert space theory is introduced and the kernel-based LSI-SVM is generated. What’s more, in order to accelerate the training speed of LSI-SVM, a stochastic gradient LSI-SVM (LSI-SVM +) is constructed using the stochastic gradient descent (SGD) solver. Lastly, experimental results on regular-scale datasets, steel surface defects datasets, ORL face dataset and large-scale datasets demonstrate that both LSI-SVM and LSI-SVM + outperform other state-of-the-art algorithms on accuracy. In the meanwhile, our LSI-SVM + has high efficiency.

结构信息对于提高分类器的分类性能非常重要。为了直接提高支持向量机（SVM）的泛化性能，提出了几种结构化的SVM。这些带有结构信息的算法仅将全局结构信息或局部类内信息简单地嵌入到SVM模型中。因此，它们有时不适合实际问题。为了克服这些缺点，我们首先提出一种具有局部结构信息的新型SVM（LSI-SVM）。在LSI-SVM中，K采用近邻（KNN）方法。应用KNN方法，获得了总体样本中距离最近的样本集内类别和最近的邻居的样本集内类别。使最近的邻居和最远的邻居之间的余量最大化是更合理的。全局和局部数据结构都被添加到优化问题中，从而使LSI-SVM可以充分利用基础结构信息并产生更好的性能。此外，对于非线性分类，引入了可再生内核希尔伯特空间理论，并生成了基于内核的LSI-SVM。此外，为了提高LSI-SVM的训练速度，使用随机梯度下降（SGD）求解器构造了随机梯度LSI-SVM（LSI-SVM +）。最后，在常规规模数据集上的实验结果，钢表面缺陷数据集，ORL面数据集和大规模数据集证明，LSI-SVM和LSI-SVM +在准确性方面均优于其他最新算法。同时，我们的LSI-SVM +具有很高的效率。