会议专题

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

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.

Support vector machine Novelty detection Kernel trick Whitening method.

Li Zhang Weida Zhou Ying Lin Licheng Jiao

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

国际会议

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

张家界

英文

41-46

2008-06-20(万方平台首次上网日期,不代表论文的发表时间)