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18th International Conference on Pattern Recognition (ICPR'06) Volume 2
Domain Based LDA and QDA
Hong Kong
August 20-August 24
ISBN: 0-7695-2521-0
ASCII Text
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Piotr Juszczak, David M.J. Tax, Serguei Verzakov, Robert P.W. Duin, "Domain Based LDA and QDA," Pattern Recognition, International Conference on, vol. 2, pp. 788-791, 18th International Conference on Pattern Recognition (ICPR'06) Volume 2, 2006.
 
 
BibTex
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@article{ 10.1109/ICPR.2006.461,
author = {Piotr Juszczak and David M.J. Tax and Serguei Verzakov and Robert P.W. Duin},
title = {Domain Based LDA and QDA},
journal ={Pattern Recognition, International Conference on},
volume = {2},
year = {2006},
issn = {1051-4651},
pages = {788-791},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.461},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - Domain Based LDA and QDA
SN - 1051-4651
SP788
EP791
A1 - Piotr Juszczak,
A1 - David M.J. Tax,
A1 - Serguei Verzakov,
A1 - Robert P.W. Duin,
PY - 2006
VL - 2
JA - Pattern Recognition, International Conference on
ER -
 
Piotr Juszczak, Imperial College London, UK
David M.J. Tax, Delft University of Technology, The Netherlands
Serguei Verzakov, Delft University of Technology, The Netherlands
Robert P.W. Duin, Delft University of Technology, The Netherlands
We propose an alternative to probability density classifiers based on normal distributions LDA and QDA. Instead of estimating covariance matrices using the standard maximum likelihood estimator we estimate class domains by the minimum volume enclosing ellipsoid (í-MVEE). The í-MVEE is a robust statistic rejecting a specified fraction í of the data. The performance of the domain and density approaches are compared in small sample size problems and in situations where sampling of a training and test sets is not i.i.d..
Citation:
Piotr Juszczak, David M.J. Tax, Serguei Verzakov, Robert P.W. Duin, "Domain Based LDA and QDA," icpr, vol. 2, pp.788-791, 18th International Conference on Pattern Recognition (ICPR'06) Volume 2, 2006
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