文章基本信息

标题：Quantile Regression Based on Laplacian Manifold Regularizer with the Data Sparsity in l1 Spaces
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作者：Ru Feng ; Shuang Chen ; Lanlan Rong 等
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2017
卷号：07
期号：05
页码：786-802
DOI：10.4236/ojs.2017.75056
语种：English
出版社：Scientific Research Publishing
摘要：In this paper, we consider the regularized learning schemes based on l ₁ -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 l ₁ -empirical covering number and boundness decomposition.
关键词：Semi-Supervised Learning;Conditional Quantile Regression;l1-Regularizer;Manifold-Regularizer;Pinball Loss