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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.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.6 pp.1431-1441

- Publication Date
- 2001/06/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- Special Section PAPER (Special Section on Papers Selected from 2000 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 2000))

- Category

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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 -