改进的基于K均值聚类的SVDD学习算法

Updated Learning Algorithm of Support Vector Data Description Based on K-Means Clustering

针对基于K均值聚类的支持向量数据描述(SVDD)学习算法(KMSVDD)识别精度低于传统SVDD学习算法的问题,提出一种改进算法。将各聚类簇中支持向量合并学习生成中间模型,从支持向量以外的非支持向量数据中找出违背中间模型KKT条件的学习数据,并将这些数据与聚类簇中支持向量合并学习继而得到最终学习模型。实验结果证明,该改进算法的计算开销与KMSVDD相近,但识别精度却高于KMSVDD,与传统SVDD相近。

Aiming at the flaw that the recognition precision of Support Vector Data Description based on K-Means（KMSVDD） clustering is lower than traditional Support Vector Data Description（SVDD）, an improvement algorithm is proposed. This algorithm learns support vectors of every cluster and produces middle model, then finds out the data against middle model＇s Karush-Kuhn-Tucker（KKT） condition from non-support vectors and obtains the final studying model by leaning them with all support vectors. Experimental result proves that this improvement algorithm has similar computing expenditure with KMSVDD and its recognizing accuracy is higher than KMSVDD and similar to traditional SVDD.