Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion

WANG Nian; WANG Junsheng; GE Fang

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(3): 521-524

WANG Nian, WANG Junsheng, GE Fang, “Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 521-524, 2013,

Citation:

WANG Nian, WANG Junsheng, GE Fang, “Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 521-524, 2013,

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Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion

Education Ministry Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Hefei 230039, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61172127), Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20113401110006), Natural Science Foundation of Anhui Province (No.1208085MF93, No.1208085QF104) and Innovative research team of 211 project in Anhui University (No.KJTD007A).

Received Date: 2012-01-01
Rev Recd Date: 2012-11-01
Publish Date: 2013-06-15

Abstract

Abstract

To deal with the insufficiency problem of Laplacian eigenmap (LE) method and Maximum margin criterion (MMC) method in feature extraction, a new dimensionality reduction method called Laplacian eigenmap based on Improved maximum margin criterion (LE/IMMC) is proposed with applications in gene expression data classification. The LE/IMMC intends to constrain similar data points as close to each other as possible and maximize the margin regions between different pattern classes simultaneously. The proposed LE/IMMC by introducing IMMC into the cost function of LE retains the characteristic of local neighborhood relationship of LE. Meanwhile, it emphasizes the discriminative information by incorporating IMMC, which can maximize the betweenclass scatter and minimize the within-class scatter. Gene expression data classification experiments on four public datasets demonstrate our method is effective for feature extraction.
- Gene expression data,
- Classification,
- Laplacian eigenmap,
- Improved maximum margin criterion

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References(13)

References

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