Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier
Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier
复制标题
结构正则化支持向量机：结构大裕度分类器的框架
DOI：
10.1109/tnn.2011.2108315
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发表时间：
2011-04-01
影响因子：
--
通讯作者：
Yang, Qiang
中科院分区：
文献类型：
--
作者：
Xue, Hui;Chen, Songcan;Yang, Qiang
支持向量机作为最流行的分类器之一，其目标是寻找一个超平面，使两类数据以最大间隔分离。SVM分类器专注于实现类之间的分离，而不是利用类内训练数据中的结构。然而，结构信息，作为一个隐含的先验知识，最近被发现是至关重要的设计一个好的分类器在不同的现实世界的问题。因此，利用数据中尽可能多的先验结构信息来帮助提高分类器的泛化能力，已经产生了一类有效的结构化大间隔分类器，例如结构化大间隔机（SLMM）和拉普拉斯支持向量机（LapSVM）。在本文中，我们统一这些分类到一个共同的框架，从“结构粒度”的概念和制定的优化问题。利用二次规划（QP）和二阶锥规划（SOCP）方法，构造了一种新的大间隔分类器，我们称之为结构正则化支持向量机（SRSVM）。与在簇粒度的交叉处的SLMM和在点粒度和QP的交叉处的SOCP和LapSVM不同，SRSVM位于簇粒度和QP的交叉处，因此遵循与LapSVM相同的优化公式，以克服SLMM中的大计算复杂度和非稀疏解。此外，它还将类内的紧性与类间的可分性统一起来。此外，它是可能的，通过使用核矩阵的特征值分析，得到这些算法的推广界。实验结果表明，SRSVM往往是上级的分类和推广性能的国家的最先进的算法的框架，无论是相同的和不同的结构粒度。
Support vector machine (SVM), as one of the most popular classifiers, aims to find a hyperplane that can separate two classes of data with maximal margin. SVM classifiers are focused on achieving more separation between classes than exploiting the structures in the training data within classes. However, the structural information, as an implicit prior knowledge, has recently been found to be vital for designing a good classifier in different real-world problems. Accordingly, using as much prior structural information in data as possible to help improve the generalization ability of a classifier has yielded a class of effective structural large margin classifiers, such as the structured large margin machine (SLMM) and the Laplacian support vector machine (LapSVM). In this paper, we unify these classifiers into a common framework from the concept of “structural granularity” and the formulation for optimization problems. We exploit the quadratic programming (QP) and second-order cone programming (SOCP) methods, and derive a novel large margin classifier, we call the new classifier the structural regularized support vector machine (SRSVM). Unlike both SLMM at the cross of the cluster granularity and SOCP and LapSVM at the cross of the point granularity and QP, SRSVM is located at the cross of the cluster granularity and QP and thus follows the same optimization formulation as LapSVM to overcome large computational complexity and non-sparse solution in SLMM. In addition, it integrates the compactness within classes with the separability between classes simultaneously. Furthermore, it is possible to derive generalization bounds for these algorithms by using eigenvalue analysis of the kernel matrices. Experimental results demonstrate that SRSVM is often superior in classification and generalization performances to the state-of-the-art algorithms in the framework, both with the same and different structural granularities.