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Hou Yuxi,이충열(Chung-Yeol Lee),박철훈(Cheol Hoon Park) 대한전자공학회 2007 대한전자공학회 학술대회 Vol.2007 No.7
Kernel Fisher Discriminant (KFD) shows good performance in pattern recognition. But as a method of feature extraction, it just adopted one-dimensional feature including the first moment information of the samples. This property badly prevent its further application in feature extraction field. In this paper, we proposed a new method to extract more features by KFD, and simulation results reveal that better performance could be obtained compared with original KFD.
Complexity-Reduced Scheme for Feature Extraction With Linear Discriminant Analysis
Yuxi Hou,Iickho Song,Hwang-Ki Min,Cheol Hoon Park IEEE 2012 IEEE transactions on neural networks and learning Vol.23 No.6
<P>Owing to the singularity of the within-class scatter, linear discriminant analysis (LDA) becomes ill-posed for small sample size (SSS) problems. Null-space-based LDA (NLDA), which is an extension of LDA, provides good discriminant performances for SSS problems. Yet, as the original scheme for the feature extractor (FE) of NLDA suffers from a complexity burden, a few modified schemes have since been proposed for complexity reduction. In this brief, by transforming the problem of finding the FE of NLDA into a linear equation problem, a novel scheme is derived, offering a further reduction of the complexity.</P>