Nonparametric Quantile Estimation: A Geometric Framework for Laplacian Manifold Regularization
- https://doi.org/10.2991/itms-15.2015.334How to use a DOI?
- Nonparametric; Quantile Regression; Manifold Regularization; Semi-supervised Learning
In this article we consider the general problem of utilizing both labeled and unlabeled data to improve quantile regression accuracy, and derive a nonparametric algorithm to compute the entire regularization path of the quantile estimator. We transform the optimization problem of the proposed approach into the quadratic optimization with linear constraint conditions and dimensionality reduction, and illustrate the finite sample behavior of the new approach.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ying Zhang PY - 2015/11 DA - 2015/11 TI - Nonparametric Quantile Estimation: A Geometric Framework for Laplacian Manifold Regularization BT - Proceedings of the 2015 International Conference on Industrial Technology and Management Science PB - Atlantis Press SP - 1364 EP - 1368 SN - 2352-538X UR - https://doi.org/10.2991/itms-15.2015.334 DO - https://doi.org/10.2991/itms-15.2015.334 ID - Zhang2015/11 ER -