title: [A novel spectrum feature extraction method].
abstract: The present focuses on the celestial spectra feature extraction problem, which is a key procedure in automatic spectra classification. By extracting features, the authors can reduce redundancy, alleviate noise influence, and improve accuracy and efficiency in spectra classification. The authors introduced a novel feature analysis framework STP (space transformation and partition), which focuses on four essential components in feature extraction: decompose and reorganize spectrum components, reorganize, alleviate noise influence and eliminate redundancy. Based on STP, we can analyze most of the available feature extraction methods, for example, the unsupervised methods principal component analysis (PCA), wavelet transform, the supervised methods support vector machine (SVM), relevance vector machine (RVM), linear discriminant analysis (LDA), etc. We introduced a novel feature analysis framework and proposed a novel feature extraction method. The outstanding characteristics of the proposed method are its simplicity and efficiency. Researches show that it is sufficient to extract features by the proposed method in some cases, and it is not necessary to use the sophisticated methods, which is usually more complex in computation. The proposed method is evaluated in classifying Galaxy and QSO spectra, which is disturbed by red shift and is representative in automatic spectra classification research. The results are practical and helpful to gain novel insight into the traditional feature extraction methods and design more efficient spectrum classification method.
authors.affiliation: School of Mathematical Sciences, South China Normal University, Guangzhou 510631, China. xiangru.li@gmail.com
authors.forename: Xiang-Ru
authors.initials: XR
authors.lastname: Li
authors.forename: Chun-Ming
authors.initials: CM
authors.lastname: Feng
authors.forename: Yong-Jun
authors.initials: YJ
authors.lastname: Wang
authors.forename: Yu
authors.initials: Y
authors.lastname: Lu