Partial Least Squares Method Based on Least Absolute Shrinkage and Selection Operator

摘要：

In many multivariate statistical techniques, a set of linear functions of the original variables is produced. But this kind of model derived is difficult to interpret, Such as principle component regression (PCR) and partial least squares regression (PLSR), they cannot select variables. The approach least absolute shrinkage and selection operator (LASSO) can easily produce sparse solutions and select variables during estimate parameters. This article proposes a new technique for interpretation based on these properties, it’s a combination of partial least squares (PLS) and LASSO and can easily interpret regression models. This method will be more favorable for large number of variables compared to PLS.

关键词： interpretation LASSO partial least squares

作者: Cuiying Li Weiguo Li

作者单位: School of Mathematics and Systems ScienceBeihang UniversityBeijing 100191, China School of Mathematics and Systems Science Beihang University Beijing 100191, China

会议类型: 国际会议

会议名称: 2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

会议地点: 成都

会议语种:英文

页码: 1-3

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）