Support vector novelty detection with dot product kernels for non-spherical data

摘要：

In this paper,a variant of support vector novelty detection (SVND)with dot product kernels is presented for non- spherical distributed data.Firstly we map the data in input space into a reproducing kernel Hilbert space (RKHS)by using kernel trick.Secondly we perform whitening process on the mapped data using kernel principal component analysis (KPCA).Finally, we adopt SVND method to train and test whitened data. Experiments were performed on artificial and real-world data.

关键词： Support vector machine Novelty detection Kernel trick Whitening method.

作者: Li Zhang Weida Zhou Ying Lin Licheng Jiao

作者单位: Institute of Intelligent Information Processing &Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi an,71071,China

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 41-46

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）