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).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.