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- W2383928320 abstract "This paper analyzed the basic principles of PCA(principal component analysis) and kPCA(kernel principal component analysis),compared the performances of kPCA and PCA in data processing,and then drew a conclusion that kPCA has more advantages than PCA in dealing with nonlinear separable data.Based on geometrical theory of diffraction(GTD),the HRRP(high resolution range profile) data were obtained by Matlab simulation method and used as the training and test sample.And then,combining with SVM classification method,performances of automatic target recognition system based on four different kernel functions were tested respectively.The ATR(automatic target recognition) system based on principal component analysis of Gaussian kernel function was obviously better than the other three ones." @default.
- W2383928320 created "2016-06-24" @default.
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- W2383928320 date "2011-01-01" @default.
- W2383928320 modified "2023-09-26" @default.
- W2383928320 title "Automatic target recognition based on kPCA feature extraction algorithm" @default.
- W2383928320 hasPublicationYear "2011" @default.
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