Table of Contents
Advances in Artificial Intelligence
Volume 2012, Article ID 484595, 7 pages
http://dx.doi.org/10.1155/2012/484595
Research Article

RPCA: A Novel Preprocessing Method for PCA

1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2Department of Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
3Electronic Research Center, Sharif University of Technology, Tehran, Iran

Received 15 May 2012; Revised 27 September 2012; Accepted 5 November 2012

Academic Editor: Wolfgang Faber

Copyright © 2012 Samaneh Yazdani 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 [5 citations]

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

  • Osamah Abdulhameed Alrezj, Krishna Chaitanya Patchava, Mohammed Benaissa, and S Alshebeili, “Coupling Scatter Correction with bandpass filtering for preprocessing in the quantitative analysis of glucose from near infrared spectra,” 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1800–1803, . View at Publisher · View at Google Scholar
  • Krishna Chaitanya Patchava, Osamah Alrezj, Mohammed Benaissa, and Hatim Behairy, “Savitzky-golay coupled with digital bandpass filtering as a pre-processing technique in the quantitative analysis of glucose from near infrared spectra,” 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6210–6213, . View at Publisher · View at Google Scholar
  • Krishna Chaitanya Patchava, Mohammed Benaissa, and Hatim Behairy, “Improving the prediction performance of PLSR using RReliefF and FSD for the quantitative analysis of glucose in Near Infrared spectra,” 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2379–2382, . View at Publisher · View at Google Scholar
  • S. Sasikala, S. Appavu alias Balamurugan, and S. Geetha, “Multi Filtration Feature Selection (MFFS) to improve discriminatory ability in clinical data set,” Applied Computing and Informatics, 2014. View at Publisher · View at Google Scholar
  • Krishna Chaitanya Patchava, Mohammed Benaissa, Bilal Malik, and Hatim Behairy, “Local linear embedded regression in the quantitative analysis of glucose in near infrared spectra,” Analytical Methods, vol. 7, no. 4, pp. 1484–1492, 2015. View at Publisher · View at Google Scholar