Error Bound of Mode-Based Additive Models
Keyword(s):
Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises, and there is also a need to improve the model’s robustness. This paper considers the estimation and variable selection via modal regression in reproducing kernel Hilbert spaces (RKHSs). Based on the mode-induced metric and two-fold Lasso-type regularizer, we proposed a sparse modal regression algorithm and gave the excess generalization error. The experimental results demonstrated the effectiveness of the proposed model.
2007 ◽
Vol 137
(3)
◽
pp. 829-840
◽
2021 ◽
Vol 500
(1)
◽
pp. 125107
2002 ◽
Vol 35
(1)
◽
pp. 103-108
◽
2013 ◽
Vol 11
(05)
◽
pp. 1350020
◽
2014 ◽
Vol 9
(4)
◽
pp. 827-931
◽
2009 ◽
Vol 80
(3)
◽
pp. 430-453
◽
2017 ◽
Vol 87
(2)
◽
pp. 225-244
◽