An Optimal Set of Uncorrelated Margin Discriminant Vectors
SUN Zheng
ZHANG Xiao-guang
XU Gui-yun
HU Xiao-lei
WANG Zhong-qing
Abstract：The usual dimensionality reduction algorithms were often affected by data distribu-tions, small size samples and others. In order to solve the problems mentioned above, an algo-rithm was proposed to obtain an optimal set of uncorrelated margin discriminant vectors by sol-ving the dual quadratic optimal problem of the modified support vector machine (SVM), which was based on the similarity between the optimal discriminant vector of linear discriminant anal-ysis (LDA) and the classification hyperplane normal vector of SVM, and the fact that the opti-mal set of uncorrelated discriminant vectors was superior to the optimal set of orthogonal dis-criminant vectors, then the algorithm was expanded to solve nonlinear feature extraction prob-lems using kernel technology. The results show that under the same parameters and k-nearest neighbor classifier for training and testing, the classification accuracy of the proposed algorithm is higher than that of SVM and recursive support vector machine(RSVM) for the public data sets Waveform, Heart, Diabetis, and doesn't appear that the classification accuracy becomes lower when the number of extraction dimensions is bigger than the optimal number, which re-flects its validity for extracting uncorrelated eigenvectors from the samples.
Keywords：support vector machine (SVM)dimensionality reductionuncorrelationoptimal margin discriminant vector
Publication Date：2009-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 856-861 )