Kernel Methods and Its Application in Wavefront Reconstruction

DOI

10.2991/iccia.2012.28How to use a DOI?

Keywords

Kernel PCA, Adaptive optics, Zernike polynomials, Alignment

Abstract

Kernel methods can effectively deal with the nonlinear problem. The methods not only can be used for data de-noising, also be effective for classification problems. Using kernel PCA method, we provide a more precise Zernike expansion, which can apparently improve the reconstruction accuracy. At the same time, explore learning the kernel function by the alignment. We verify that the alignment value and recognition rate is proportional relationship.

Copyright

© 2013, 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  - Zhiying Tan
AU  - Ying Chen
AU  - Kun She
AU  - Yong Feng
PY  - 2014/05
DA  - 2014/05
TI  - Kernel Methods and Its Application in Wavefront Reconstruction
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 117
EP  - 120
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.28
DO  - 10.2991/iccia.2012.28
ID  - Tan2014/05
ER  -