波谱学杂志Issue(1)：67-77,11.DOI:10.11938/cjmr20150108
L1范数支持向量机在代谢组学中的应用
L1-Norm Support Vector Machine and Its Application in Metabonomics
摘要
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.关键词
模式识别/L1范数支持向量机(L1-norm SVM)/正交偏最小二乘(O-PLS)/代谢组学/核磁共振(NMR)Key words
pattern recognition/L1-norm support vector machine/orthogonal partial least squares/metabonomics/nuclear magnetic resonance分类
数理科学引用本文复制引用
丁国辉,孙建强,吴俊芳,黄慎,丁义明..L1范数支持向量机在代谢组学中的应用[J].波谱学杂志,2015,(1):67-77,11.基金项目
国家青年自然科学基金资助项目(21105115)． （21105115）
