Stellar spectral recognition based on wavelet de-noising and SVM

The present paper describes a new technique for stellar spectral recognition. Considering the characteristics of stellar spectral data, support vector machine (SVM) was adopted to build a recognition system as kernel. Because stellar spectral data sets are usually extremely noisy, the correct classification rate of direct applying SVM is low. Consequently, wavelet de-noising method was proposed to reduce noise first and extract the main characteristics of stellar spectra. Then SVM was used for the recognition. Based on the real-world stellar spectra contributed by Jacoby et al. (1984), it has proven that there will be a better performance using this composite classifier which combines wavelet and SVM than using SVM with principle component analysis data dimension reduction technique. From the experiment of comparison of discriminant analysis and SVM based on stellar spect ra for evolutionary synthesis, we can see that the correct classification rate of SVM is higher than that of discriminant analysis methods, and a well generalization ability is achieved.