局部分块的一类支持向量数据描述

One-class support vector data description based on local patch

针对支持向量数据描述(SVDD)不能鉴别数据局部几何结构信息问题,提出了一种新颖的异常数据检测方法,称为局部分块的一类支持向量数据描述(OCSVDDLP)。首先对数据进行局部分块,然后利用局部分块进行样本重构,最后采用SVDD对重构样本进行学习进而获得决策模型。人造数据集实验结果表明OCSVDDLP能够捕捉数据的全局几何结构,也具备揭示数据局部几何结构信息的能力;真实数据集实验结果表明OCSVDDLP在异常检测中具有较好的性能优势。

Because Support Vector Data Description（ SVDD） fails in identifying the local geometric information, a new detection method, called One-class SVDD based on Local Patch（ OCSVDDLP）, was proposed. First, the data was divided into many local patches. Then, each sample was reconstructed by using the corresponding local patch. Finally, the decision model was obtained through training on the reconstruction data with SVDD. The experimental results on the artificial data set demonstrate that OCSVDDLPcan not only capture the global geometric structure of the data set, but also uncover the local geometric information. Besides, the results on real-world data sets validate the effectiveness of the proposed method.