کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
417233 681474 2008 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Robustified L2 boosting
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Robustified L2 boosting
چکیده انگلیسی

Five robustifications of L2L2 boosting for linear regression with various robustness properties are considered. The first two use the Huber loss as implementing loss function for boosting and the second two use robust simple linear regression for the fitting in L2L2 boosting (i.e. robust base learners). Both concepts can be applied with or without down-weighting of leverage points. Our last method uses robust correlation estimates and appears to be most robust. Crucial advantages of all methods are that they do not compute covariance matrices of all covariates and that they do not have to identify multivariate leverage points. When there are no outliers, the robust methods are only slightly worse than L2L2 boosting. In the contaminated case though, the robust methods outperform L2L2 boosting by a large margin. Some of the robustifications are also computationally highly efficient and therefore well suited for truly high-dimensional problems.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 52, Issue 7, 15 March 2008, Pages 3331–3341
نویسندگان
, , ,