流形判别分析和支持向量机的恒星光谱数据自动分类方法  

Automatic Classification Method of Star Spectra Data Based on Manifold-Based Discriminant Anaysis and Support Vector Machine

作　　者：刘忠宝[1] 王召巴[2] 赵文娟[3] 

机构地区：[1]中北大学计算机与控制工程学院,山西太原030051 [2]中北大学信息与通信工程学院,山西太原030051 [3]山西大学商务学院信息学院,山西太原030031

出　　处：《光谱学与光谱分析》2014年第1期263-266,共4页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61202311);山西省高等学校科技创新项目(20131112)资助

摘　　要：尽管经典的分类方法支持向量机SVM在天文学领域广泛应用,但其只考虑类间的绝对间隔而忽略类内的分布性状,因而分类性能有待于进一步提升。鉴于此,提出一种新颖的基于流形判别分析和支持向量机的恒星光谱数据自动分类方法。该方法引入流形判别分析的两个重要概念:基于流形的类内离散度MW和基于流形的类间离散度MB。所提方法找到的分类面同时保证MW最小且MB最大。可建立相应最优化问题,然后将原最优化问题转化为QP对偶形式求得支持向量和判别函数,最后利用判别函数判断测试样本的类属。该方法的最大优势在于进行分类决策时,不仅考虑样本的类间信息和分布特征,而且还保持了各类的局部流形结构。SDSS恒星光谱数据上的比较实验表明该方法的有效性。Although Support Vector Machine （SVM） is widely used in astronomy, it only takes the margin between classes into consideration while neglects the data distribution in each class, which seriously limits the classification efficiency. In view of this, a novel automatic classification method of star spectra data based on manifold-based discriminant analysis （MDA） and SVM is proposed in this paper. Two important concepts in MDA, manifold-based within-class scatter （MWCS） and manifold-based between-class scatter （~）, are introduced in the proposed method, the separating hyperplane found by which ensures MWCS is minimized and MBCS is maximized. Based on the above analysis, the corresponding optimal problem can be estab- lished, and then MDA transforms the original optimization problem to the QP dual form and we can obtain the support vectors and decision function. The classes of test samples are decided by the decision function. The advantage of the proposed method is that it not only focuses on the information between classes and distribution characteristics, but also preserves the manifold struc- ture of each class. Experiments on SDSS star spectra datasets verify the effectiveness of the proPosed method.

关 键 词：自动分类 恒星光谱数据 流形判别分析 支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]