Target Focus Regularization as Compared with Ill-Posed Weighted Total Least Square
Abstract Usually in ill-posed error-in-variable models, only part of columns in the design matrix possess multicollinearity. This is where the model focuses. According to the focus characteristics, the corresponding regularization strategy is made, which can overcome the ill-posed effects and minimize side effects caused by the process of regularization at the same time. We propose target focus regularization as opposed to the ill-posed error-in-variable model. Numerical experiments show the proposed regularized total least-squares solution method performs better than others.
Key words :
serror-in-variable model
total least squares
multicollinearity
data columns related to disturbing
regularization
target focus
Cite this article:
GU Yongwei,GUI Qingming,ZHAO Jun. Target Focus Regularization as Compared with Ill-Posed Weighted Total Least Square[J]. jgg, 2016, 36(3): 253-256.
GU Yongwei,GUI Qingming,ZHAO Jun. Target Focus Regularization as Compared with Ill-Posed Weighted Total Least Square[J]. jgg, 2016, 36(3): 253-256.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2016/V36/I3/253
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