基于Fisher判别分析的有监督特征提取和星系光谱分类

随着天文观测技术的进步、数据获取能力的提高和大型光谱巡天计划的相继实施，光谱数据的自动处理研究越来越受到重视和关注。文章在分析了文献中光谱自动分类研究的特点和无监督特征提取方法所固有的一些不足的基础上指出了光谱有监督特征提取研究的必要性。并重点研究了Fisher判别分析(FDA)有监督特征提取方法在星系光谱自动分类中的应用。该方法： (1) 具有突出的维数约减能力; (2) 能有效地融合训练数据的类别信息，并按照分类能力提取特征。实验表明，将FDA方法用于某些星系细分类不仅明显地提高了分类器的速度，而且具有良好的分类性能。因此，对于较大的光谱识别系统更能体现出该方法的优越性。

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.FDA could effectively reduce dimension and extract the features based on the classifying capability by fusing information in training data.Experiments show its superior performance in dimensional reduction for galaxy spectra classification.