Computer Science – Performance
Scientific paper
Nov 2007
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007spie.6788e..75y&link_type=abstract
MIPPR 2007: Pattern Recognition and Computer Vision. Edited by Maybank, S. J.; Ding, Mingyue; Wahl, F.; Zhu, Yaoting. Proceedin
Computer Science
Performance
Scientific paper
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.
Liu Zhongtian
Wu Fuchao
Yang Jinfu
No associations
LandOfFree
Efficient stellar spectral type classification for SDSS based on nonnegative matrix factorization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Efficient stellar spectral type classification for SDSS based on nonnegative matrix factorization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Efficient stellar spectral type classification for SDSS based on nonnegative matrix factorization will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1547820