Chinese Journal of Magnetic Resonance >
L1-Norm Support Vector Machine and Its Application in Metabonomics
Received date: 2014-06-20
Revised date: 2015-01-12
Online published: 2015-03-05
Supported by
国家青年自然科学基金资助项目(21105115)．
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.
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 . DOI: 10.11938/cjmr20150108
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