Genetic Generalized Discriminant Analysis and Its Applications

Lijun Yan; Lin-Lin Tang; Shu-Chuan Chu; Xiaorui Zhu; Jun-Bao Li; Xiaochuan Guo

doi:10.1007/978-3-319-07455-9_26

Genetic Generalized Discriminant Analysis and Its Applications

Lijun Yan, Lin-Lin Tang, Shu-Chuan Chu, Xiaorui Zhu, Jun-Bao Li, Xiaochuan Guo

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

In this paper, a novel Genetic Generalized Discriminant Analysis (GGDA) is proposed. GGDA is a generalized version of Exponential Discriminant Analysis (EDA). EDA algorithm is equivalent to map the samples to a new space and then perform LDA. However, is this space is optimal for classification? The proposed GGDA uses Genetic Algorithm to search for an more discriminant diffusing map and then perform LDA in the new space. The Experimental results confirm the efficiency of the proposed algorithm.

Original language	English
Pages	246-255
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-319-07455-9_26
Publication status	Published - 1 Jan 2014
Event	The 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014 - Duration: 3 Jun 2014 → …

Conference

Conference	The 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014
Period	3/06/14 → …

Keywords

Exponential discriminant analysis
Feature extraction
Genetic algorithm

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10.1007/978-3-319-07455-9_26

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title = "Genetic Generalized Discriminant Analysis and Its Applications",

abstract = "In this paper, a novel Genetic Generalized Discriminant Analysis (GGDA) is proposed. GGDA is a generalized version of Exponential Discriminant Analysis (EDA). EDA algorithm is equivalent to map the samples to a new space and then perform LDA. However, is this space is optimal for classification? The proposed GGDA uses Genetic Algorithm to search for an more discriminant diffusing map and then perform LDA in the new space. The Experimental results confirm the efficiency of the proposed algorithm.",

keywords = "Exponential discriminant analysis, Feature extraction, Genetic algorithm",

author = "Lijun Yan and Lin-Lin Tang and Shu-Chuan Chu and Xiaorui Zhu and Jun-Bao Li and Xiaochuan Guo",

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doi = "10.1007/978-3-319-07455-9_26",

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T1 - Genetic Generalized Discriminant Analysis and Its Applications

AU - Yan, Lijun

AU - Tang, Lin-Lin

AU - Chu, Shu-Chuan

AU - Zhu, Xiaorui

AU - Li, Jun-Bao

AU - Guo, Xiaochuan

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In this paper, a novel Genetic Generalized Discriminant Analysis (GGDA) is proposed. GGDA is a generalized version of Exponential Discriminant Analysis (EDA). EDA algorithm is equivalent to map the samples to a new space and then perform LDA. However, is this space is optimal for classification? The proposed GGDA uses Genetic Algorithm to search for an more discriminant diffusing map and then perform LDA in the new space. The Experimental results confirm the efficiency of the proposed algorithm.

AB - In this paper, a novel Genetic Generalized Discriminant Analysis (GGDA) is proposed. GGDA is a generalized version of Exponential Discriminant Analysis (EDA). EDA algorithm is equivalent to map the samples to a new space and then perform LDA. However, is this space is optimal for classification? The proposed GGDA uses Genetic Algorithm to search for an more discriminant diffusing map and then perform LDA in the new space. The Experimental results confirm the efficiency of the proposed algorithm.

KW - Exponential discriminant analysis

KW - Feature extraction

KW - Genetic algorithm

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U2 - 10.1007/978-3-319-07455-9_26

DO - 10.1007/978-3-319-07455-9_26

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T2 - The 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014

Y2 - 3 June 2014

ER -

Genetic Generalized Discriminant Analysis and Its Applications

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Keywords

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