%0 Conference Paper %1 1378562 %A Lu, Cong-De %A Zhang, Tai-Yi %A Du, Xing-Zhong %A Li, Can-Ping %B Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on %D 2004 %K kernel multivariate outliers pca robust %P 3084-3087 vol.5 %R 10.1109/ICMLC.2004.1378562 %T A robust kernel PCA algorithm %U http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1378562&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1378562 %V 5 %X This paper presents a novel algorithm - robust kernel principal component analysis (robust KPCA), on the basis of the research of kernel principal component analysis (KPCA) and robust principal component analysis (RPCA). First, this algorithm sets the radius of the images of the training samples in the feature space using kernel tricks, then determines whether the samples are outliers or not, and finally analyzes the training samples which have eliminated the outliers using KPCA algorithm. The improved KPCA algorithm not only retains the non-linearity property of KPCA algorithm but also gets better robustness. Because the effects of outliers are eliminated, robust KPCA algorithm gets higher accuracy than KPCA algorithm for data analysis. The simulation experiments show that the robust KPCA algorithm developed is better than the KPCA algorithm.

@inproceedings{1378562, abstract = {This paper presents a novel algorithm - robust kernel principal component analysis (robust KPCA), on the basis of the research of kernel principal component analysis (KPCA) and robust principal component analysis (RPCA). First, this algorithm sets the radius of the images of the training samples in the feature space using kernel tricks, then determines whether the samples are outliers or not, and finally analyzes the training samples which have eliminated the outliers using KPCA algorithm. The improved KPCA algorithm not only retains the non-linearity property of KPCA algorithm but also gets better robustness. Because the effects of outliers are eliminated, robust KPCA algorithm gets higher accuracy than KPCA algorithm for data analysis. The simulation experiments show that the robust KPCA algorithm developed is better than the KPCA algorithm.}, added-at = {2013-10-01T15:32:32.000+0200}, author = {Lu, Cong-De and Zhang, Tai-Yi and Du, Xing-Zhong and Li, Can-Ping}, biburl = {https://www.bibsonomy.org/bibtex/27a88c03a858f41d8fa053b77e8b88783/vivion}, booktitle = {Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on}, description = {IEEE Xplore - A robust kernel PCA algorithm}, doi = {10.1109/ICMLC.2004.1378562}, interhash = {17d79f4dab2858602290830d7df20c4c}, intrahash = {7a88c03a858f41d8fa053b77e8b88783}, keywords = {kernel multivariate outliers pca robust}, pages = {3084-3087 vol.5}, timestamp = {2013-10-01T15:32:32.000+0200}, title = {A robust kernel PCA algorithm}, url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1378562&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1378562}, volume = 5, year = 2004 }