摘要
针对线性回归模型,提出了一个新的期望递归最小二乘算法(Expectation Recursive Least Square,ERLS)。在响应变量数据存在部分缺失的条件下,ERLS取响应变量的期望值代替缺失值,基于该期望值与自变量数据,实现自适应的递归估计回归系数,避免了高维数据相关矩阵的求逆困难。ERLS算法充分利用了全部有效数据,实现了在线回归估计。数值实验结果表明,在观测数据存在野值时,通过引入非线性抑制函数,ERLS算法优于LS方法。
A novel Expectation Least Square（ERLS） algorithm is proposed for linear regression model.Under the condition that response is partly missing,ERLS uses expectation value of the response instead of the missing value.Based on the expectation value and the data of independent variable,ERLS adaptively estimates the regression coefficients,which avoids the difficulty of inversion operation to the correlation matrix of high-dimensional data.ERLS makes fully use of the available data and sovles the regression problem in an online manner.Numerical expriments show that,by introducing a nonlinear function of supression,ERLS is superior to LS solution under the existence of wild data points.
出处
《大连民族学院学报》
CAS
2012年第5期469-473,共5页
Journal of Dalian Nationalities University
基金
国家自然科学基金项目(61002039)
中央高校基本科研业务费专项资金资助项目(DC12010216)
