面向大规模数据集的单类支持向量机

One-class support vector machines for large-scale data sets

提出了一种面向大规模数据集的单类支持向量机(OCSVM)方法.该方法基于k近邻思想得到表征数据集合分布特征的集合内点,并依此生成集合边缘点,而后由二者重新组成数据集合,用于OCSVM训练.该新建数据集不仅极大压缩了原有大规模数据集的样本数量,还可以保留原有大规模数据集的分布特征,从而有效解决了OCSVM在处理大规模数据集时所存在的训练时间长、模型复杂以及预测效率低等问题.最后,通过在典型数据集合上进行的对比实验,表明了所提方法的有效性.

A method to train one-class support vector machine（OCSVM） on the large-scale data sets is proposed.The proposed method selects inner points representing the distribution characteristics of the original large-scale data sets based on the principle of k-nearest neighbor,and generates the edge points using the inner points selected.A new data set is formed by combining these tw o kinds of points to train OCSVM.The new data set not only reduces the volume of the original large-scale data set greatly,but also maintains the distribution characteristics of the original data set.Thus the problems faced by OCSVM on the large-scale data sets,such as long training time,complicated models and low predicting speed,are effectively solved.Finally,the experiment is conducted on typical data sets to illustrate the effectiveness of the method proposed.