RVM Supervised Feature Extraction and Seyfert Spectra Classification

	RVM Supervised Feature Extraction and Seyfert Spectra Classification
	Li Xiang-ru 1,2,5; Hu Zhan-yi1 ; Zhao Yong-heng 3; Li Xiao-ming 4
发表期刊	SPECTROSCOPY AND SPECTRAL ANALYSIS
	2009-06-01
卷号	29 期号:6 页码:1702-1706
文章类型	Article
摘要	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 Therefore, research on automated classification methods based on celestial spectra has been attracting more and more attention in recent years. Feature extraction is a fundamental problem in automated spectral classification, which not only influences the difficulty and complexity of the problem, but also determines the performance of the designed classifying system. The available methods of feature extraction for spectra classification are usually unsupervised, e. g. principal components analysis (PCA), wavelet transform (WT), artificial neural networks (ANN) and Rough Set theory. These methods extract features not by their capability to classify spectra, but by some kind of power to approximate the original celestial spectra. Therefore, the extracted features by these methods usually are not the best ones for classification. In the present work, the authors pointed out the necessary to investigate supervised feature extraction by analyzing the characteristics of the spectra classification research in available literature and the limitations of unsupervised feature extracting methods. And the authors also studied supervised feature extracting based on relevance vector machine (RVM) and its application in Seyfert spectra classification. RVM is a recently introduced method based on Bayesian methodology, automatic relevance determination (ARD), regularization technique and hierarchical priors structure. By this method, the authors can easily fuse the information in training data, the authors' prior knowledge and belief in the problem, etc. And RVM could effectively extract the features and reduce the data based on classifying capability. Extensive experiments show its superior performance in dimensional reduction and feature extraction for Seyfert classification.
关键词	Seyfert Spectra Classification Spectra Feature Extraction Bayesian Learning Relevance Vector Machine (Rvm)
WOS标题词	Science & Technology ; Technology
关键词[WOS]	SELECTION
收录类别	SCI
语种	英语
WOS研究方向	Spectroscopy
WOS类目	Spectroscopy
WOS记录号	WOS:000266681400062
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/2967
专题	多模态人工智能系统全国重点实验室_机器人视觉
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China 2.Shandong Univ Sci & Technol, Coll Informat & Elect Engn, Qingdao 266510, Peoples R China 3.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China 4.Shanxi Univ, Sch Math Sci, Taiyuan 030006, Peoples R China 5.S China Normal Univ, Sch Math Sci, Guangzhou 510631, Guangdong, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Li Xiang-ru,Hu Zhan-yi,Zhao Yong-heng,et al. RVM Supervised Feature Extraction and Seyfert Spectra Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2009,29(6):1702-1706.
APA	Li Xiang-ru,Hu Zhan-yi,Zhao Yong-heng,&Li Xiao-ming.(2009).RVM Supervised Feature Extraction and Seyfert Spectra Classification.SPECTROSCOPY AND SPECTRAL ANALYSIS,29(6),1702-1706.
MLA	Li Xiang-ru,et al."RVM Supervised Feature Extraction and Seyfert Spectra Classification".SPECTROSCOPY AND SPECTRAL ANALYSIS 29.6(2009):1702-1706.