Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers

✍ Scribed by Gavin C. Cawley; Nicola L.C. Talbot

• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 194 KB
• Volume: 36
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/s0031-3203(03)00136-5

✦ Synopsis

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, where ' is the number of training patterns. Leave-one-out cross-validation then becomes an attractive means of model selection in large-scale applications of kernel Fisher discriminant analysis, being signiÿcantly faster than conventional k-fold cross-validation procedures commonly used.