Kelin Xu
MOE Key Laboratory of Contemporary Anthropology, School of Life-sciences, Fudan University, Shanghai 200433, China
Key words: Genetic data, high dimension hazard, variable selection, dimension reduction
Recieved: Dec.7, 2011 Accepted: Dec.11, 2011 Corresponding: xukelin0202@gmail.com
《现代人类学通讯》第六卷e4篇 第26-29页 2012年1月17日网上发行
专题综述
遗传数据中的变量选择与降维
徐珂琳
复旦大学 生命科学学院 现代人类学教育部重点实验室, 上海200433
摘要:随着新一代测序技术与芯片杂交技术的发展,海量高维数据涌现在研究者们的面前,如何从这些高维数据中提取有效信息成为摆在人们面前的一大难题。在这种高维问题的背景下,许多基于低纬度的统计结论不再成立;另外,庞大的数据量对计算速度提出了很高的要求。于是,在这种数据驱动的研究背景下,变量选择与降维成为主要的研究方向。本文从当下遗传数据的特点出发,回顾了当今几种主流的变量选择与降维方法,如主成分分析、偏最小二乘回归、切片逆回归、LASSO等,并就这几种方法的性质与适用范围展开讨论。
关键词:遗传数据;高维问题;变量选择;降维
收稿日期:2011年12月7日
修回日期:2011年12月11日
联系人:徐珂琳
xukelin0202@gmail.com
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