RVM Supervised Feature Extraction and Seyfert Spectra Classification
LI Xiang-ru1, 2, 5,HU Zhan-yi1,ZHAO Yong-heng3,LI Xiao-ming4
1. Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China 2. College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266510, China 3. National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China 4. School of Mathematical Science, Shanxi University, Taiyuan 030006, China 5. School of Mathematical Science, South China Normal University, Guangzhou 510631, China
Abstract：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.
[1] Kurtz M J. Progress in Automation Techniques for MK Classification. The MK Process and Stellar Classification, ed. Garrison R F, Proceedings of the Workshop in honour of Morgan W W, Keenan P C, 1984. [2] LI Xiang-ru, WU Fu-chao, HU Zhan-yi, et al(李乡儒，吴福朝，胡占义，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2005，25(11)：1889. [3] LI Xiang-ru, LIU Zhong-tian, HU Zhan-yi, et al(李乡儒, 刘中田, 胡占义, 等) . Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(7): 1448. [4] LIU Zhong-tian, LI Xiang-ru, WU Fu-chao, et al(刘中田, 李乡儒, 吴福朝, 等). Acta Electronica Sinica(电子学报), 2007, 35(1): 157. [5] Castander F J, Nichol R C. Astronomical Journal, 2001, 121: 2331C. [6] QIU Bo, HU Zhan-yi, ZHAO Yong-heng(邱 波，胡占义，赵永恒). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2002, 22(3): 523. [7] Cheeseman P, Stutz J. Bayesian Classification(AutoClass): Theory and Results. In Advances in Knowledge Discovery and Data Mining, Usama M F, Gregory P S, Padhraic S, et al, Eds. AAAI Press/MIT Press, 1996. [8] LI Xiang-ru, HU Zhan-yi, ZHAO Yong-heng(李乡儒, 胡占义, 赵永恒). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(9): 1898. [9] Tipping M E. Journal of Machine Learning Research, 2001, 1: 211. [10] Krishnapuram B, Hartemink A, Carin L, et al. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9): 1105. [11] Figueiredo M. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25: 1150. [12] Tibshirani R. Journal of the Royal Statistical Society, Series B, 1996, 58(1): 267. [13] Lei H, Michael A S, et al. The Astronomical Journal, 2005, 129: 1783. [14] ZHAO Mei-fang, WU Chao, LUO A-li, et al(赵梅芳, 吴 潮, 罗阿理, 等). Acta Astronomica Sinica(天文学报), 2007, 48(1): 1. [15] LI Xiang-ru, HU Zhan-yi, ZHAO Yong-heng, et al(李乡儒，胡占义, 赵永恒, 等). Acta Astronomica Sinica(天文学报), 2007, 48(3): 280.
