{ "_id": "6a2515074b233be19838b20d", "Package": "kfda", "Type": "Package", "Title": "Kernel Fisher Discriminant Analysis", "Version": "1.0.0", "Date": "2017-09-27", "Author": "Donghwan Kim", "Maintainer": "Donghwan Kim ", "Description": "Kernel Fisher Discriminant Analysis (KFDA) is performed\nusing Kernel Principal Component Analysis (KPCA) and Fisher\nDiscriminant Analysis (FDA). There are some similar packages.\nFirst, 'lfda' is a package that performs Local Fisher\nDiscriminant Analysis (LFDA) and performs other functions. In\nparticular, 'lfda' seems to be impossible to test because it\nneeds the label information of the data in the function\nargument. Also, the 'ks' package has a limited dimension, which\nmakes it difficult to analyze properly. This package is a\nsimple and practical package for KFDA based on the paper of\nYang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. 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