کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
536521 | 870547 | 2011 | 5 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: On the equivalence of Kernel Fisher discriminant analysis and Kernel Quadratic Programming Feature Selection On the equivalence of Kernel Fisher discriminant analysis and Kernel Quadratic Programming Feature Selection](/preview/png/536521.png)
We reformulate the Quadratic Programming Feature Selection (QPFS) method in a Kernel space to obtain a vector which maximizes the quadratic objective function of QPFS. We demonstrate that the vector obtained by Kernel Quadratic Programming Feature Selection is equivalent to the Kernel Fisher vector and, therefore, a new interpretation of the Kernel Fisher discriminant analysis is given which provides some computational advantages for highly unbalanced datasets.
► Kernelization of the quadratic programming feature selection (QPFS) algorithm.
► Proof of the equivalence with Kernel Fisher discriminant (KFD).
► New solution and interpretation of the KFD direction.
► More efficient computation of KFD vector when the classes are highly unbalanced.
Journal: Pattern Recognition Letters - Volume 32, Issue 11, 1 August 2011, Pages 1567–1571