中文摘要：

特征提取是光谱自动识别中的一个基本问题,它决定着光谱识别系统的性能和复杂度。目前的天体光谱自动分类研究主要使用的是基于以线性主成分分析（PCA）、小波变换（Wavelet transform）、人工神经网络（ANN）等为代表的非监督特征提取方法,而它们在特征提取时没有考虑到训练数据中的类别信息,并非按照分类能力进行特征选择和降维。文章研究了相关向量机（RVM）有监督特征提取方法及其在Seyfert光谱细分类中的应用。RVM是机器学习领域在近几年提出的一种Bayesian学习方法,它能有效地融合已有的先验知识、对问题的信念、训练数据和相应的类别信息,并按照分类能力提取特征,在理论上具有很大的潜在优势。另外,初步的实验结果表明,基于RVM的有监督特征提取方法在Seyfert光谱细分类中具有较好的性能。

英文摘要：

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 （P CA）, 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 rele- vance 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.