Kernel ICA Feature Extraction for Spectral Recognition of Celestial Objects

In the literature of astronomical spectral classification, linear principle component analysis (PCA) was frequently employed to extract features of spectra data. However, the spectral data are too complicated to be well described by a linear model. In this paper, kernel independent component analysis (KICA), which contains a nonlinear kernel mapping component, is adopted to extract features from the spectra of galaxies. Then, a radial basis function neural network is adopted as a classifier to implement the classification. Experiments with real-world spectral data set show that KICA is a very appropriate technique to describe the important features of celestial objects, and the correct classification rate is improved compared with PCA method.