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Proceedings Paper

A kernel density estimation based Bayesian classifier for celestial spectrum recognition
Author(s): Jin-fu Yang; Ming-ai Li; Naigong Yu
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Paper Abstract

Celestial spectrum recognition is an indispensable part of any workable automated data processing system of celestial objects. Many methods have been proposed for spectra recognition, in which most of them concerned about feature extraction. In this paper, we present a Bayesian classifier based on Kernel Density Estimation (KDE) which is composed of the following two steps: In the first step, linear Principle Component Analysis (PCA) is used to extract features to decrease computational complexity and make the distribution of spectral data more compact and useful for classification. In the second step, namely classification step, KDE and Expectation Maximum (EM) algorithm are used to estimate class conditional density and the bandwidth of kernel function respectively. The experimental results show that the proposed method can achieve satisfactory performance over the real observational data of Sloan Digital Sky Survey (SDSS).

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749611 (30 October 2009); doi: 10.1117/12.833958
Show Author Affiliations
Jin-fu Yang, Beijing Univ. of Technology (China)
Ming-ai Li, Beijing Univ. of Technology (China)
Naigong Yu, Beijing Univ. of Technology (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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