SVM-Lattice: A Recognition & Evaluation Frame for Double-peaked Profiles

• URL: http://arxiv.org/abs/2005.00678v1
• Date: Sat, 2 May 2020 01:56:18 GMT
• Title: SVM-Lattice: A Recognition & Evaluation Frame for Double-peaked Profiles
• Authors: Haifeng Yang, Caixia Qu, Jianghui Cai, Sulan Zhang, Xujun Zhao
• Abstract summary: A new lattice structure named SVM-Lattice is designed based on SVM and FCL. SVM-Lattice is particularly applied in the recognition and evaluation of rare spectra with double-peaked profiles. The results exhibit good consistency with traditional methods, more detailed and accurate evaluations of classification results, and higher searching efficiency than other similar methods.
• Score: 5.2708048125255615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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