With the rapid growth in astronomical spectra produced by large sky survey telescopes, traditional manual classification processes can no longer fulfill the requirements of precision and efficiency of spectral classification. There is an urgent need to employ machine learning approaches to conduct automated spectral classification tasks. Feature extraction is a critical step which has a great impact on any classification result. In this paper, a novel gradient-based method together with principal component analysis is proposed for the extraction of partial features of stellar spectra, that is, a feature vector indicating obvious local changes in data, which corresponds to the element line positions in the spectra. Furthermore, a general feature vector is utilized as an additional characteristic centering on the overall tendency of spectra, which can indicate stellar effective temperature. The two feature vectors and raw data are input into three neural networks, respectively, for training and each network votes for a predicted category of spectra. By selecting the class having the maximum votes, different types of spectra can be classified with high accuracy. The experimental results prove that a better performance can be achieved using the partial and general methods in this paper. The method could also be applied to other similar one-dimensional spectra, and the concepts proposed could ultimately expand the scope of machine learning application in astronomical spectral processing.

中文翻译：

---

使用部分和一般方法的光谱特征提取

随着大型巡天望远镜产生的天文光谱的快速增长，传统的人工分类过程已不能满足光谱分类精度和效率的要求。迫切需要采用机器学习方法来执行自动光谱分类任务。特征提取是关键步骤，对任何分类结果都有很大影响。在本文中，提出了一种新的基于梯度的方法结合主成分分析来提取恒星光谱的部分特征，即表示数据局部变化明显的特征向量，它对应于光谱中的元素线位置。 . 此外，一般特征向量被用作以光谱整体趋势为中心的附加特征，可以指示恒星的有效温度。两个特征向量和原始数据分别输入到三个神经网络中进行训练，每个网络对预测的光谱类别进行投票。通过选择具有最大票数的类别，可以高精度地对不同类型的光谱进行分类。实验结果证明，使用本文的部分方法和一般方法可以获得更好的性能。该方法也可以应用于其他类似的一维光谱，所提出的概念最终可以扩大机器学习在天文光谱处理中的应用范围。通过选择具有最大票数的类别，可以高精度地对不同类型的光谱进行分类。实验结果证明，使用本文的部分方法和一般方法可以获得更好的性能。该方法也可以应用于其他类似的一维光谱，所提出的概念最终可以扩大机器学习在天文光谱处理中的应用范围。通过选择具有最大票数的类别，可以高精度地对不同类型的光谱进行分类。实验结果证明，使用本文的部分方法和一般方法可以获得更好的性能。该方法也可以应用于其他类似的一维光谱，所提出的概念最终可以扩大机器学习在天文光谱处理中的应用范围。