Given n training examples, the training of a Kernel Fisher Discriminant (KFD) classifier corresponds to solving a linear system of dimension n. In cross-validating KFD, the training examples are split into 2 distinct subsets for a number of times (L) wherein a subset of m examples is used for validation and the other subset of(n - m) examples is used for training the classifier. In this case L linear systems of dimension (n - m) need to be solved. We propose a novel method for cross-validation of KFD in which instead of solving L linear systems of dimension (n - m), we compute the inverse of an n × n matrix and solve L linear systems of dimension 2m, thereby reducing the complexity when L is large and/or m is small. For typical 10-fold and leave-one-out cross-validations, the proposed algorithm is approximately 4 and (4/9n) times respectively as efficient as the naive implementations. Simulations are provided to demonstrate the efficiency of the proposed algorithms.
History
Pagination
22 - 27
Location
Los Angeles, Calif.
Open access
Yes
Start date
2005-12-15
End date
2005-12-17
ISBN-13
9780769524955
ISBN-10
0769524958
Language
eng
Notes
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Publication classification
E1.1 Full written paper - refereed
Copyright notice
2005, IEEE
Title of proceedings
ICMLA 2005 : Proceedings of the 4th International Conference on Machine Learning and Applications