Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics

Exploration of Approach to Mining WDMS Spectra based on Laplacian Eigenmap and Neural Network

Authors
Bin Jiang, Zixuan Li, Wenyu Wang, Meixia Qu
Corresponding Author
Bin Jiang
Available Online June 2015.
DOI
https://doi.org/10.2991/icecee-15.2015.187How to use a DOI?
Keywords
Laplacian Eigenmap; Data mining; BPNN
Abstract
For the purpose of discovering White Dwarf +Main Sequence (WDMS) from massive spectra, in this paper, an unsupervised learning algorithm for Nonlinear Dimensionality Reduction named Laplacian Eigenmap is discussed. It turns out that, comparing with Principle Component Analysis (PCA), Laplacian Eigenmap maintains the information of nonlinear structure of high dimensional spectral data, which leads to a higher classification accuracy. In the feature space, backpropagation neural network is used to classify WDMS and non-WDMS spectra. Furthermore, Particle Swarm Optimization (PSO) is implemented to increase the classification accuracy via optimizing the parameters of the network. The results shows that the method in this paper can discover WDMS efficiently and accurately after training the neural network with low-dimensional data from Sloan Digital Sky Survey Data Release 10 (SDSS-DR10).
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2015 2nd International Conference on Electrical, Computer Engineering and Electronics
Part of series
Advances in Computer Science Research
Publication Date
June 2015
ISBN
978-94-62520-81-3
ISSN
2352-538X
DOI
https://doi.org/10.2991/icecee-15.2015.187How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Bin Jiang
AU  - Zixuan Li
AU  - Wenyu Wang
AU  - Meixia Qu
PY  - 2015/06
DA  - 2015/06
TI  - Exploration of Approach to Mining WDMS Spectra based on Laplacian Eigenmap and Neural Network
BT  - 2015 2nd International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 986
EP  - 991
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.187
DO  - https://doi.org/10.2991/icecee-15.2015.187
ID  - Jiang2015/06
ER  -