Margin maximizing hyperplanes based enhanced feature extraction algorithm
HOU Yong1,2,ZHENG Xuefeng2
1. College of Humanities and Science, Shandong Vocational College of Economics and Business, Weifang Shandong 261011, China
2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083 China
Abstract：Kernel Principal Component Analysis (KPCA) and Multi-Layer Perceptron (MLP) neural network are popular feature extraction algorithms. However, these algorithms are inefficient and easy to fall into local optimal solution. The paper proposed a new feature extraction algorithm — margin maximizing hyperplanes based Enhanced Feature Extraction algorithm （EFE）, which can overcome the problem of KPCA and MLP algorithm. The proposed EFE algorithm, whcih maps the input samples to the subspace spanned by the normals of hyperplanes through adopting the pairwise orthogonal margin maximizing hyperplanes, is independent of the probability distribution of the input samples. The results of these feature extraction experiments on real world data set — wine and AR show that FE algorithm is beyond KPCA and MLP in terms of the efficiency of the implementation and accuracy of recognition.
2013, Vol. 33 
