SVM-Lattice：一种双峰轮廓的识别与评价框架
SVM-Lattice: A Recognition & Evaluation Frame for Double-peaked Profiles
摘要：
在大数据时代，具有罕见特征的特殊数据可能具有重要意义。然而，从海量高维数据集中自动搜索这些样本并对其进行系统评价是非常困难的。我们的前期工作[2]提出了一种从LAMOST观测的海量高维数据中搜索具有双峰轮廓的稀有光谱的方法。结果的确认主要依靠天文学家的目视检查。作为后续研究，本文基于支持向量机（SVM）和形式概念格（FCL）设计了一种新的格结构SVM-Lattice，并将其应用于具有双峰轮廓的稀有光谱的识别和评价。首先，SVM-Lattice结构中的每个节点包含两个部分：意图由具有特定特征的谱样本训练的支持向量定义，相关程度由支持向量分类的所有正样本定义。超平面可以从每个格节点中提取，并用作分类器来按类别搜索目标。层与层之间表达了一种泛化和特化关系，越高的层表示目标的置信度越高。然后，给出了支持向量机格的构造算法、基于关联规则的剪枝算法和评价算法，并对其进行了分析。最后，利用LAMOST巡天的数据集作为实验数据集，对具有双峰轮廓的光谱进行了识别和评价。该方法与传统方法具有良好的一致性，对分类结果的评价更加细致、准确，搜索效率也高于其他同类方法。
英文摘要：
In big data era, the special data with rare characteristics may be of great significations. However, it is very difficult to automatically search these samples from the massive and high-dimensional datasets and systematically evaluate them. The DoPS, our previous work [2], provided a search method of rare spectra with double-peaked profiles from massive and high-dimensional data of LAMOST survey. The identification of the results is mainly depended on visually inspection by astronomers. In this paper, as a follow-up study, a new lattice structure named SVM-Lattice is designed based on SVM(Support Vector Machine) and FCL(Formal Concept Lattice) and particularly applied in the recognition and evaluation of rare spectra with double-peaked profiles. First, each node in the SVM-Lattice structure contains two components: the intents are defined by the support vectors trained by the spectral samples with the specific characteristics, and the relevant extents are all the positive samples classified by the support vectors. The hyperplanes can be extracted from every lattice node and used as classifiers to search targets by categories. A generalization and specialization relationship is expressed between the layers, and higher layers indicate higher confidence of targets. Then, including a SVM-Lattice building algorithm, a pruning algorithm based on association rules, and an evaluation algorithm, the supporting algorithms are provided and analysed. Finally, for the recognition and evaluation of spectra with double-peaked profiles, several data sets from LAMOST survey are used as experimental dataset. The results exhibit good consistency with traditional methods, more detailed and accurate evaluations of classification results, and higher searching efficiency than other similar methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
