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

Author

Zhang, Li ; Zhou Weida ; Lin, Ying ; Jiao, Licheng

Author_Institution

Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ., Xidian Univ., Xi´´an

fYear

2008

fDate

20-23 June 2008

Firstpage

41

Lastpage

46

Abstract

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.

Keywords

Hilbert spaces; principal component analysis; support vector machines; Kernel trick; dot product kernels; kernel principal component analysis; novelty detection; reproducing kernel Hilbert space; support vector machine; whitening method; Automation; Educational products; Hilbert space; Information processing; Kernel; Laboratories; Lagrangian functions; Principal component analysis; Testing; Training data; Kernel trick; Novelty detection; Support vector machine; Whitening method;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation, 2008. ICIA 2008. International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-2183-1

Electronic_ISBN

978-1-4244-2184-8

Type

conf

DOI

10.1109/ICINFA.2008.4607965

Filename

4607965