Deakin University
Browse
venkatesh-fastcross-2005.pdf (172.47 kB)

Fast cross-validation of kernel fisher discriminant classifiers

Download (172.47 kB)
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

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

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

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC