Publication detail

Wavelet Based Feature Extraction for Clustering of Be Stars

VRÁBELOVÁ, P. ŠKODA, P. ZENDULKA, J.

Original Title

Wavelet Based Feature Extraction for Clustering of Be Stars

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

The goal of our work is to create a feature extraction method for classification of Be stars. Be stars are characterized by prominent emission lines in their spectrum. We focus on the automated classification of Be stars based on typical shapes of their emission lines. We aim to design a reduced, specific set of features characterizing and discriminating the shapes of Be lines. In this paper, we present a feature extraction method based on the wavelet transform and its power spectrum. Both the discrete and continuous wavelet transform are used. Different feature vectors are created and compared on clustering of Be stars spectra from the archive of the Astronomical Institute of the Academy of Sciences of the Czech Republic. The clustering is performed using the k- means algorithm. The results of our method are promising and encouraging to more detailed analysis.

Keywords

Be star, feature extraction, wavelet transform, wavelet power spectrum

Authors

VRÁBELOVÁ, P.; ŠKODA, P.; ZENDULKA, J.

RIV year

2013

Released

31. 3. 2013

Publisher

Springer US

Location

New York

ISBN

978-3-319-00541-6

Book

Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

Edition

Volume 210 of Advances in Intelligent Systems and Computing

Pages from

467

Pages to

474

Pages count

9

URL

http://link.springer.com/chapter/10.1007%2F978-3-319-00542-3_46

BibTex

@inproceedings{BUT106552,
  author="Pavla {Vrábelová} and Petr {Škoda} and Jaroslav {Zendulka}",
  title="Wavelet Based Feature Extraction for Clustering of Be Stars",
  booktitle="Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems",
  year="2013",
  series="Volume 210 of Advances in Intelligent Systems and Computing",
  pages="467--474",
  publisher="Springer US",
  address="New York",
  doi="10.1007/978-3-319-00542-3\{_}46",
  isbn="978-3-319-00541-6",
  url="http://link.springer.com/chapter/10.1007%2F978-3-319-00542-3_46"
}