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Advances in Artificial Intelligence
Volume 2012 (2012), Article ID 484595, 7 pages
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.


We propose a preprocessing method to improve the performance of Principal Component Analysis (PCA) for classification problems composed of two steps; in the first step, the weight of each feature is calculated by using a feature weighting method. Then the features with weights larger than a predefined threshold are selected. The selected relevant features are then subject to the second step. In the second step, variances of features are changed until the variances of the features are corresponded to their importance. By taking the advantage of step 2 to reveal the class structure, we expect that the performance of PCA increases in classification problems. Results confirm the effectiveness of our proposed methods.