Table of Contents
ISRN Computational Mathematics
Volume 2012, Article ID 197352, 13 pages
Research Article

Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification

1Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canada
2Mathematics and Statistics Department, University of Guelph, Guelph, ON, Canada N1G 2W1
3Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK

Received 3 May 2012; Accepted 13 June 2012

Academic Editors: L. Hajdu, L. S. Heath, R. A. Krohling, E. Weber, and W. G. Weng

Copyright © 2012 Shengkun Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [4 citations]

The following is the list of published articles that have cited the current article.

  • Zhao Lu, and Wen Yan, “Closed-form projection operator wavelet kernels in support vector learning for nonlinear dynamical systems identification,” The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, . View at Publisher · View at Google Scholar
  • Shengkun Xie, Anna T. Lawniczak, and Sridhar Krishnan, “Noise Effects on Spatial Pattern Data Classification Using Wavelet Kernel PCA,” Advances in Neural Networks – ISNN 2013, vol. 7951, pp. 273–282, 2013. View at Publisher · View at Google Scholar
  • I. G. Chouvarda, D. Babalis, V. Papaioannou, N. Maglaveras, and D. Georgopoulos, “Multiparametric modeling of the ineffective efforts in assisted ventilation within an ICU,” Medical & Biological Engineering & Computing, 2015. View at Publisher · View at Google Scholar
  • Zhao Lu, Jing Sun, and Kenneth Butts, “Multiscale Support Vector Learning With Projection Operator Wavelet Kernel for Nonlinear Dynamical System Identification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 1, pp. 231–243, 2017. View at Publisher · View at Google Scholar