Efficient stellar spectral type classification for SDSS based on nonnegative matrix factorization

The problem of identifying spectra collected by large sky survey telescope is urgent to study to help astronomers discover new celestial bodies. Due to spectral data characteristics of high-dimension and volume, principle component analysis (PCA) technique is commonly used for extracting features and saving operations. Like many other matrix factorization methods, PCA lacks intuitive meaning because of its negativity. In this paper, non-negative matrix factorization (NMF) technique distinguished from PCA by its use of nonnegative constrains is applied to stellar spectral type classification. Firstly, NMF was used to extract features and compress data. Then an efficient classifier based on distance metric was designed to identify stellar types using the compressed data. The experiment results show that the proposed method has good performance over more than 70,000 real stellar data of Sloan Digital Sky Survey (SDSS). And the method is promising for large sky survey telescope projects.