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Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers
2003
Pattern Recognition
Mika et al. (in: Neural Network for Signal Processing, Vol. IX, IEEE Press, New York, 1999; pp. 41-48) apply the "kernel trick" to obtain a non-linear variant of Fisher's linear discriminant analysis method, demonstrating state-of-the-art performance on a range of benchmark data sets. We show that leave-one-out cross-validation of kernel Fisher discriminant classiÿers can be implemented with a computational complexity of only O(' 3 ) operations rather than the O(' 4 ) of a na ve implementation,
doi:10.1016/s0031-3203(03)00136-5
fatcat:dzuueoby6vamtnv6nv2b4hi2aq