L1-Norm Support Vector Machine and Its Application in Metabonomics

doi:10.11938/cjmr20150108

Abstract

Abstract:

Metabonomics analyzes metabolite profiles in living systems and its dynamic responses to changes of endogenous (i.e., physiology and development) and exogenous(i.e., environment and xenobiotics) factors. Pattern recognition plays an important role in data-processing in metabonomic. L₁-norm support vector machine (L₁-norm SVM) is an accurate and robust method in pattern recognition, but not widely used in metabonomics. In this study, we used L₁-norm SVM to analyze metabonomic data obtained from mice infected by schistosomiasis. It was shown that L₁-norm SVM had better performance than
orthogonal partial least squares (O-PLS) in terms of clustering and feature selection. The results also showed that support vector machines have great potential and prospects for data-processing in metabonomics.

Key words: pattern recognition, L₁-norm support vector machine, orthogonal partial least squares, metabonomics, nuclear magnetic resonance

CLC Number:

O482.53

DING Guo-hui1,2,4，SUN Jian-qiang1,2,4，WU Jun-fang1,2,3，HUANG Shen1,2,4，DING Yi-ming1,2. L₁-Norm Support Vector Machine and Its Application in Metabonomics[J]. Chinese Journal of Magnetic Resonance, 2015, 32(1): 67-77.

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L₁-Norm Support Vector Machine and Its Application in Metabonomics

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