Classification of OGLE Eclipsing Binary Stars Based on Their Morphology Type with Locally Linear Embedding

Approximately 375 classification spectra of 130 Southern Hemisphere eclipsing binary stars were obtained between 1978 and 1982 at Mt. John University Observatory, New Zealand using the 0.6 meter reflector, and at Cerro Tololo Inter-American Observatory, Chile using the 0.4, 0.6, 0.9, and 1.0 meter telescopes. The spectra have been classified by one of us (GWW) using a grid of standards obtained on the various spectrographs at each of the observatories. Since many of the spectra were taken during primary and secondary minima, it has been possible in many cases to classify separately each component in the binaries.

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Automatic classification of eclipsing binary stars using deep learning methods

Astronomy and Computing ◽

10.1016/j.ascom.2021.100488 ◽

2021 ◽

pp. 100488

Author(s):

M. Čokina ◽

V. Maslej-Krešňáková ◽

P. Butka ◽

Š. Parimucha

Keyword(s):

Deep Learning ◽

Binary Stars ◽

Automatic Classification ◽

Learning Methods ◽

Eclipsing Binary ◽

Eclipsing Binary Stars

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KEPLERECLIPSING BINARY STARS. III. CLASSIFICATION OFKEPLERECLIPSING BINARY LIGHT CURVES WITH LOCALLY LINEAR EMBEDDING

The Astronomical Journal ◽

10.1088/0004-6256/143/5/123 ◽

2012 ◽

Vol 143 (5) ◽

pp. 123 ◽

Cited By ~ 93

Author(s):

Gal Matijevič ◽

Andrej Prša ◽

Jerome A. Orosz ◽

William F. Welsh ◽

Steven Bloemen ◽

...

Keyword(s):

Binary Stars ◽

Light Curves ◽

Locally Linear Embedding ◽

Linear Embedding ◽

Locally Linear

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Application of Locally Linear Embedding Based on Improved Distance in Neuron Classification

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.2996 ◽

2014 ◽

Vol 926-930 ◽

pp. 2996-2999

Author(s):

Zhen Zhen Wang ◽

Xiao Jun Tong ◽

Shan Zeng

Keyword(s):

Euclidean Distance ◽

Locally Linear Embedding ◽

Support Vector ◽

Morphological Parameters ◽

Svm Algorithm ◽

Good Classification ◽

Linear Embedding ◽

Sample Component ◽

Locally Linear

For locally linear embedding (LLE) algorithm of the shortcoming, an improved distance algorithm LLE is proposed, in locally linear embedding algorithm the distribution of sample component is different and the Euclidean distance can’t reflect sample distance actually. In the experiment, a sample of 231 neurons is obtained, and the morphological parameters of neurons are calculated firstly. Second, the improved locally linear embedding algorithm is used to reduce data dimensionality. Finally, support vector machine (SVM) algorithm is used to train and test samples. Experimental results show under certain conditions the classification of the method has good classification.

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