Title :
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
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;
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
DOI :
10.1109/ICINFA.2008.4607965
