[RVM supervised feature extraction and Seyfert spectra classification].

With recent technological advances in wide field survey astronomy and implementation of several large-scale astronomical survey proposals (e. g. SDSS, 2dF and LAMOST), celestial spectra are becoming very abundant and rich.

[Supervised feature extraction based on FDA and galaxy spectra classification].

With the recent technological advances in wide field survey astronomy and the implementation of several large scale astronomical survey proposals, celestial spectra are becoming very rich and the study of automated processing methods is attracting more and more attention. In the present work, the authors pointed out that it is necessary to investigate supervised feature extraction by analyzing the characteristics of the spectra classification research in literature and the limitations of unsupervised feature extraction methods. And the authors studied supervised feature extraction based on Fisher discriminant analysis (FDA) and its application in galaxy spectra classification.

[Celestial spectrum flux standardization for classification].

Celestial spectra should be preprocessed before automated classification to eliminate the disturbance of noise, observa-tion environment, and flux aberrance. In the present work, the authors studied the spectrum flux standardization problem. By analyzing the disturbing factors and their characteristics, the authors put forward a theoretical model for spectra flux, and corre-spondingly give several flux standardizing methods.

[Wavelet NeighShrink method for grid texture removal in image of solar radio bursts].

The data received from solar bursts contain a lot of noise, which makes further processing more difficult. To remove the noise and enhance the image, we studied the properties of the NeighShrink threshold function and analyzed the influence of neighborhood window size on the denoising result, on the basis of which a new wavelet NeighShrink square root method for image denoising is presented. Firstly, each channel of the solar burst image is normalized, which can, to some extent, remove the horizontal grid texture in the image.

[A novel method for the determination of redshifts of normal galaxies by non-linear dimensionality reduction].

It is difficult to determine the redshifts of normal galaxies (NG) from their spectra because of their common weak absorption property. In the present work, a novel method is proposed to effectively deal with this issue. The proposed method is composed of the following three parts: At first, the wavelet transform coefficients at the fourth scaling are experimentally found to be appropriate and used as our features to represent the absorption information from NG absorption lines, break points, and absorption bands.

[Automated recognition of quasars based on adaptive radial basis function neural networks].

Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy. This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks (RBFN). The proposed method is composed of the following three parts: (1) The feature space is reduced by the PCA (the principal component analysis) on the normalized input spectra; (2) An adaptive RBFN is constructed and trained in this reduced space.

[A novel spectral classifier based on coherence measure].

Classification and discovery of new types of celestial bodies from voluminous celestial spectra are two important issues in astronomy, and these two issues are treated separately in the literature to our knowledge. In the present paper, a novel coherence measure is introduced which can effectively measure the coherence of a new spectrum of unknown type with the training sampleslocated within its neighbourhood, then a novel classifier is designed based on this coherence measure. The proposed classifier is capable of carrying out spectral classification and knowledge discovery simultaneously.

[Wavelet transform zero-crossing method for auto-extraction of spectral lines].

Wavelet transform method is used in this paper. The authors firstly studied the properties of spectral lines in the wavelet domain. By introducing the items of up-zero-crossings and down-zero-crossings, the authors concluded that absorption lines and emission lines correspond to different kinds of zero-crossings.

[Automated classification of celestial spectra based on support vector machines].

The main objective of an automatic recognition system of celestial objects via their spectra is to classify celestial spectra and estimate physical parameters automatically. This paper proposes a new automatic classification method based on support vector machines to separate non-active objects from active objects via their spectra. With low SNR and unknown red-shift value, it is difficult to extract true spectral lines, and as a result, active objects can not be determined by finding strong spectral lines and the spectral classification between non-active and active objects becomes difficult.

[A new automatic quasars recognition technique based on PCA and Hough transform].

The main purpose of quasar recognition is to determine the observed quasar spectrum's redshift value. Previously the template of quasar rest frame in the literature was basically constructed based on astronomers' hypotheses. Due to the inaccuracy of such a template, it is hard to determine the redshift value by matching the observed quasar spectrum with the template directly.

[A PCA based efficient stellar spectra classification method].

Stellar spectra classification is an indispensable part of any workable automated recognition system of celestial bodies. This paper introduces an efficient method of automated classification of stellar spectra based on the principal component analysis (PCA). The method consists of two parts.

[A pseudo-triangle technique for redshift identification of celestial spectra].

In this paper, we present a novel technique for redshift identification. Redshift is a key parameter of celestial spectra. In the literature, there are few reports on redshift identification due to either no many people working on the problem or perhaps industrial confidentiality.

[Describing language of spectra and rough set].

It is the traditional way to analyze spectra by experiences in astronomical field. And until now there has never been a suitable theoretical frame to describe spectra, which is may be owing to small spectra datasets that astronomers can get by low-level instruments. With the high-speed development of telescopes, especially on behalf of LAMOST, a large telescope which can collect more than 20,000 spectra in an observing night, spectra datasets are becoming larger and larger very fast.