In this paper, we investigate the Principal Component Analysis-Optimal Discrimination Plane (PCA-ODP) approach on a data set of galaxy spectra including eleven standard subtypes with the redshift value ranging from 0 to 1.2 and a span of 0.001. These eleven subtypes are E, S0, Sa, Sb, Sc, SB1, SB2, SB3, SB4, SB5, SB6, respectively, according to the Hubble sequence. Among them, the first four subtypes belong to the class of normal galaxies (NGs); the remaining seven belong to active galaxies (AGs). We apply the PCA approach to extract the features of galaxy spectra, project the samples onto the PCs, and investigate the ODP method on the data of feature space to find the optimal discrimination plane of the two main classes. ODP approach was developed from Fisher's linear discriminant method. The difference between them is that Fisher's method uses only one Fisher's vector and ODP uses two orthogonal vectors including Fisher's vector and another. Besides the data set above, we also use the Sloan Digital Sky Survey (SDSS) galaxy spectra and Kennicutt (1992) galaxy data to test the ODP classifier. The experiment results show that our proposed technique is both robust and efficient. The correct rate can reach as high as 99.95% for the first group data, 96% for SDSS data and 98% for Kennicutt data.
Stellar spectra classification is an indispensable part of any workable automated recognition system of celestial bodies. Like other celestial spectra, stellar spectra are also extremely noisy and voluminous; consequently, any acceptable technique of classification must be both computationally efficient and robust to structural noise. In this paper, we propose a practical stellar spectral classification technique which is composed of the following three steps: In the first step, the Haar wavelet transform is used to extract spectral lines, then followed by a de-noising process by the hard thresholding in the wavelet field. As a result, in the subsequent steps, only those extracted spectral lines are used for classification due to the high reliability of spectral lines with respect to the continuum. In the second step, the Principal Component Analysis (PCA) is employed for optimal data compression. More specifically, we use 165 well-selected samples from 7 spectral classes of stellar spectra to construct the 'eigen-lines spectra' by PCA. Thirdly, unknown spectra are projected to the eigen-subspace defined by the above eigen-lines spectra, and then a fuzzy c-means algorithm is used for the final classification. The experiments show that our new technique is both robust and efficient.