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    • Stellar Spectral Classification with Locality Preserving Projections and Support Vector Machine

      Liu Zhong-bao

      Volume 37 Issue 2 June 2016 Article ID 0000012

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      Permanent link:
      https://www.ias.ac.in/article/fulltext/joaa/037/02/0000012

    • Keywords

       

      Stellar spectral classification; locality preserving projections (LPP); support vector machine (SVM).

    • Abstract

       

      With the help of computer tools and algorithms, automatic stellar spectral classification has become an area of current interest. The process of stellar spectral classification mainly includes two steps: dimension reduction and classification. As a popular dimensionality reduction technique, Principal Component Analysis (PCA) is widely used in stellar spectra classification. Another dimensionality reduction technique, Locality Preserving Projections (LPP) has not been widely used in astronomy. The advantage of LPP is that it can preserve the local structure of the data after dimensionality reduction. In view of this, we investigate how to apply LPP+SVM in classifying the stellar spectral subclasses. In the comparative experiment, the performance of LPP is compared with PCA. The stellar spectral classification process is composed of the following steps. Firstly, PCA and LPP are respectively applied to reduce the dimension of spectra data. Then, Support Vector Machine (SVM) is used to classify the 4 subclasses of K-type and 3 subclasses of F-type spectra from Sloan Digital Sky Survey (SDSS). Lastly, the performance of LPP+SVM is compared with that of PCA+SVM in stellar spectral classification, and we found that LPP does better than PCA.

    • Author Affiliations

       

      Liu Zhong-bao1

      1. School of Computer and Control Engineering, North University of China, Taiyuan 030051, China.

    • Dates

       
      Published
      1 June 2016
      Manuscript received
      5 January 2016
      Accepted
      25 February 2016

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