Fast Algorithm for Online Linear Discriminant Analysis
Summary :
Linear discriminant analysis (LDA) is a basic tool of pattern recognition, and it is used in extensive fields, e.g. face identification. However, LDA is poor at adaptability since it is a batch type algorithm. To overcome this, new algorithms of online LDA are proposed in the present paper. In face identification task, it is experimentally shown that the new algorithms are about two times faster than the previously proposed algorithm in terms of the number of required examples, while the previous algorithm attains better final performance than the new algorithms after sufficient steps of learning. The meaning of new algorithms are also discussed theoretically, and they are suggested to be corresponding to combination of PCA and Mahalanobis distance.
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- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.6 pp.1431-1441
- Publication Date
- 2001/06/01
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- Special Section PAPER (Special Section on Papers Selected from 2000 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 2000))
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Kazuyuki HIRAOKA, Masashi HAMAHIRA, Ken-ichi HIDAI, Hiroshi MIZOGUCHI, Taketoshi MISHIMA, Shuji YOSHIZAWA, "Fast Algorithm for Online Linear Discriminant Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 6, pp. 1431-1441, June 2001, doi: .
Abstract: Linear discriminant analysis (LDA) is a basic tool of pattern recognition, and it is used in extensive fields, e.g. face identification. However, LDA is poor at adaptability since it is a batch type algorithm. To overcome this, new algorithms of online LDA are proposed in the present paper. In face identification task, it is experimentally shown that the new algorithms are about two times faster than the previously proposed algorithm in terms of the number of required examples, while the previous algorithm attains better final performance than the new algorithms after sufficient steps of learning. The meaning of new algorithms are also discussed theoretically, and they are suggested to be corresponding to combination of PCA and Mahalanobis distance.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_6_1431/_p
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@ARTICLE{e84-a_6_1431,
author={Kazuyuki HIRAOKA, Masashi HAMAHIRA, Ken-ichi HIDAI, Hiroshi MIZOGUCHI, Taketoshi MISHIMA, Shuji YOSHIZAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fast Algorithm for Online Linear Discriminant Analysis},
year={2001},
volume={E84-A},
number={6},
pages={1431-1441},
abstract={Linear discriminant analysis (LDA) is a basic tool of pattern recognition, and it is used in extensive fields, e.g. face identification. However, LDA is poor at adaptability since it is a batch type algorithm. To overcome this, new algorithms of online LDA are proposed in the present paper. In face identification task, it is experimentally shown that the new algorithms are about two times faster than the previously proposed algorithm in terms of the number of required examples, while the previous algorithm attains better final performance than the new algorithms after sufficient steps of learning. The meaning of new algorithms are also discussed theoretically, and they are suggested to be corresponding to combination of PCA and Mahalanobis distance.},
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - Fast Algorithm for Online Linear Discriminant Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1431
EP - 1441
AU - Kazuyuki HIRAOKA
AU - Masashi HAMAHIRA
AU - Ken-ichi HIDAI
AU - Hiroshi MIZOGUCHI
AU - Taketoshi MISHIMA
AU - Shuji YOSHIZAWA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2001
AB - Linear discriminant analysis (LDA) is a basic tool of pattern recognition, and it is used in extensive fields, e.g. face identification. However, LDA is poor at adaptability since it is a batch type algorithm. To overcome this, new algorithms of online LDA are proposed in the present paper. In face identification task, it is experimentally shown that the new algorithms are about two times faster than the previously proposed algorithm in terms of the number of required examples, while the previous algorithm attains better final performance than the new algorithms after sufficient steps of learning. The meaning of new algorithms are also discussed theoretically, and they are suggested to be corresponding to combination of PCA and Mahalanobis distance.
ER -
English
