KFDA

(redirected from Kernel Fisher Discriminant Analysis)
AcronymDefinition
KFDAKorea Food & Drug Administration
KFDAKernel Fisher Discriminant Analysis (computer science)
KFDAKunsten Festival des Arts (Brussels, Belgium art festival)
KFDAKansas Funeral Directors and Embalmers Association
References in periodicals archive ?
Recently, Arbia Soula et al presented in [7] and [8] a novel face recognition systems formulated on Gabor and ordinal filters for feature extrication, and on the Kernel Fisher Discriminant Analysis (KFD) and the Kernel Nonparametric Discriminant Analysis (KNDA), respectively, for dimension reduction and classification.
Kernel Fisher discriminant analysis (KFDA) [10] and Kernel principal component analysis (KPCA) [11] are classical nonlinear feature extraction methods with dimensionality reduction.
Xu, "Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel Fisher discriminant analysis," International Journal of Advanced Manufacturing Technology, vol.
Vandewalle, "Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel fisher discriminant analysis," Neural Computation, vol.
In this paper, the Full Space Kernel Fisher Discriminant Analysis (FSKFDA) is used for feature reduction [10].
The above linear methods have been extended to nonlinear ones after kernel method presented [15-20], such as kernel principal component analysis (KPCA), kernel partial least squares (KPLS), and kernel fisher discriminant analysis (KFDA).
Improving Kernel Fisher Discriminant Analysis for Face Recognition, IEEE Trans.
Tahir, "Non-sparse multiple kernel fisher discriminant analysis," Journal of Machine Learning Research, vol.
Liu, "Learning kernel parameters for kernel Fisher discriminant analysis," Pattern Recognition Letters, vol.
Many statistic methods such as principal component analysis (PCA) [19, 20], linear discriminant analysis (LDA) [21, 22], kernel principal component Analysis (KPCA) [23] and kernel fisher discriminant analysis (KFDA) [24] have been widely investigated due to high efficiency to capture variance information among the training set.
The representative methods are kernel principal component analysis (KPCA) [4] and kernel Fisher discriminant analysis (KFDA) [5], which are the kernel extensions of PCA and LDA, respectively.
The approach has been studied and extended some kernel based algorithms such as kernel principal component analysis (KPCA) [29] and kernel fisher discriminant analysis (KFD)[30, 31].