TITLE:
Quantile Regression Based on Laplacian Manifold Regularizer with the Data Sparsity in l1 Spaces
AUTHORS:
Ru Feng, Shuang Chen, Lanlan Rong
KEYWORDS:
Semi-Supervised Learning, Conditional Quantile Regression, l1-Regularizer, Manifold-Regularizer, Pinball Loss
JOURNAL NAME:
Open Journal of Statistics,
Vol.7 No.5,
October
23,
2017
ABSTRACT: In this paper, we consider the regularized learning
schemes based on l1-regularizer
and pinball loss in a data dependent hypothesis space. The target is the error
analysis for the quantile regression learning. There is no regularized
condition with the kernel function, excepting continuity and boundness.
The graph-based semi-supervised algorithm leads to an extra error term called
manifold error. Part of new error bounds and convergence rates are exactly
derived with the techniques consisting of l1-empirical
covering number and boundness decomposition.