Detail publikace

Wavelet Based Feature Extraction for Clustering of Be Stars

VRÁBELOVÁ, P. ŠKODA, P. ZENDULKA, J.

Originální název

Wavelet Based Feature Extraction for Clustering of Be Stars

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Rok RIV

2013

Vydáno

31. 3. 2013

Nakladatel

Springer US

Místo

New York

ISBN

978-3-319-00541-6

Kniha

Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

Edice

Volume 210 of Advances in Intelligent Systems and Computing

Strany od

467

Strany do

474

Strany počet

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"
}