Nonparametric Quantile Estimation: A Geometric Framework for Laplacian Manifold Regularization

DOI

10.2991/itms-15.2015.334How to use a DOI?

Keywords

Nonparametric; Quantile Regression; Manifold Regularization; Semi-supervised Learning

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/itms-15.2015.334
ID  - Zhang2015/11
ER  -