Proceedings of the 2015 International Conference on Industrial Technology and Management Science

Nonparametric Quantile Estimation: A Geometric Framework for Laplacian Manifold Regularization

Authors
Ying Zhang
Corresponding Author
Ying Zhang
Available Online November 2015.
DOI
https://doi.org/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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Volume Title
Proceedings of the 2015 International Conference on Industrial Technology and Management Science
Series
Advances in Computer Science Research
Publication Date
November 2015
ISBN
978-94-6252-123-0
ISSN
2352-538X
DOI
https://doi.org/10.2991/itms-15.2015.334How to use a DOI?
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/).

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  -