Extracting the optimal dimensionality for discriminant analysis

Feiping Nie; Shiming Xiang; Yangqiu Song; Changshui Zhang

doi:10.1109/ICASSP.2007.366311

Extracting the optimal dimensionality for discriminant analysis

Feiping Nie, Shiming Xiang, Yangqiu Song, Changshui Zhang

Tsinghua University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

For classification task, supervised dimensionality reduction is a very important method when facing with high-dimensional data. Linear Discriminant Analysis(LDA) is one of the most popular method for supervised dimensionality reduction. However, LDA suffers from the singularity problem, which makes it hard to work. Another problem is the determination of optimal dimensionality for discriminant analysis, which is an important issue but often been neglected previously. In this paper, we propose a new algorithm to address these two problems. Experiments show the effectiveness of our method and demonstrate much higher performance in comparison to LDA.

Original language	English
Title of host publication	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Pages	II617-II620
DOIs	https://doi.org/10.1109/ICASSP.2007.366311
State	Published - 2007
Externally published	Yes
Event	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States Duration: 15 Apr 2007 → 20 Apr 2007

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2
ISSN (Print)	1520-6149

Conference

Conference	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/Territory	United States
City	Honolulu, HI
Period	15/04/07 → 20/04/07

Keywords

Image recognition
Linear discriminant analysis
Optimal dimensionality
Singularity problem
Supervised dimensionality reduction

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10.1109/ICASSP.2007.366311

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Nie, F., Xiang, S., Song, Y., & Zhang, C. (2007). Extracting the optimal dimensionality for discriminant analysis. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 (pp. II617-II620). Article 4217484 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2). https://doi.org/10.1109/ICASSP.2007.366311

@inproceedings{7248d6b26ec1459bbb07b66fe8524e8c,

title = "Extracting the optimal dimensionality for discriminant analysis",

abstract = "For classification task, supervised dimensionality reduction is a very important method when facing with high-dimensional data. Linear Discriminant Analysis(LDA) is one of the most popular method for supervised dimensionality reduction. However, LDA suffers from the singularity problem, which makes it hard to work. Another problem is the determination of optimal dimensionality for discriminant analysis, which is an important issue but often been neglected previously. In this paper, we propose a new algorithm to address these two problems. Experiments show the effectiveness of our method and demonstrate much higher performance in comparison to LDA.",

keywords = "Image recognition, Linear discriminant analysis, Optimal dimensionality, Singularity problem, Supervised dimensionality reduction",

author = "Feiping Nie and Shiming Xiang and Yangqiu Song and Changshui Zhang",

year = "2007",

doi = "10.1109/ICASSP.2007.366311",

language = "英语",

isbn = "1424407281",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "II617--II620",

booktitle = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07",

note = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 ; Conference date: 15-04-2007 Through 20-04-2007",

}

Nie, F, Xiang, S, Song, Y & Zhang, C 2007, Extracting the optimal dimensionality for discriminant analysis. in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07., 4217484, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2, pp. II617-II620, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, Honolulu, HI, United States, 15/04/07. https://doi.org/10.1109/ICASSP.2007.366311

Extracting the optimal dimensionality for discriminant analysis. / Nie, Feiping; Xiang, Shiming; Song, Yangqiu et al.
2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07. 2007. p. II617-II620 4217484 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Xiang, Shiming

AU - Song, Yangqiu

AU - Zhang, Changshui

PY - 2007

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N2 - For classification task, supervised dimensionality reduction is a very important method when facing with high-dimensional data. Linear Discriminant Analysis(LDA) is one of the most popular method for supervised dimensionality reduction. However, LDA suffers from the singularity problem, which makes it hard to work. Another problem is the determination of optimal dimensionality for discriminant analysis, which is an important issue but often been neglected previously. In this paper, we propose a new algorithm to address these two problems. Experiments show the effectiveness of our method and demonstrate much higher performance in comparison to LDA.

AB - For classification task, supervised dimensionality reduction is a very important method when facing with high-dimensional data. Linear Discriminant Analysis(LDA) is one of the most popular method for supervised dimensionality reduction. However, LDA suffers from the singularity problem, which makes it hard to work. Another problem is the determination of optimal dimensionality for discriminant analysis, which is an important issue but often been neglected previously. In this paper, we propose a new algorithm to address these two problems. Experiments show the effectiveness of our method and demonstrate much higher performance in comparison to LDA.

KW - Image recognition

KW - Linear discriminant analysis

KW - Optimal dimensionality

KW - Singularity problem

KW - Supervised dimensionality reduction

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DO - 10.1109/ICASSP.2007.366311

M3 - 会议稿件

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BT - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07

T2 - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07

Y2 - 15 April 2007 through 20 April 2007

ER -

Extracting the optimal dimensionality for discriminant analysis

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