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Fast cross-validation of kernel fisher discriminant classifiers

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conference contribution
posted on 2005-01-01, 00:00 authored by S An, W Liu, Svetha VenkateshSvetha Venkatesh
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

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