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- W2373410300 abstract "For the standard KCCA method in the case of training samples increases the complexity of the corresponding surge in the computer memory occupied by a large quantity of defects,based on the standard derivation of KCCA feature extraction methods,this paper proposed an improved feature extraction method of nuclear function.In this method,the value based on characteristics of the training sample size to judge the degree of importance,and then completed the corresponding eigenvectors,and combined with the SVDD classifier,the image recognition rate in the case of little effect to enhance the features of the image extraction efficiency,saving the system's storage capacity,and finally by the Yale face database standard simulation comparison experiments were carried out to verify the feasibility of the method.Thus improves the efficiency of image pattern recognition provides an effective way." @default.
- W2373410300 created "2016-06-24" @default.
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- W2373410300 date "2011-01-01" @default.
- W2373410300 modified "2023-09-23" @default.
- W2373410300 title "Feature extraction based on kernel function and application of improved methods" @default.
- W2373410300 hasPublicationYear "2011" @default.
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