RPCA: a novel preprocessing method for PCA

Authors:
Samaneh Yazdani;Jamshid Shanbehzadeh;Mohammad Taghi Manzuri Shalmani
Affiliations:
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran;Department of Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran;Electronic Research Center, Sharif University of Technology, Tehran, Iran
Venue:
Advances in Artificial Intelligence
Year:
2012

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Abstract

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.