流形判別分析和支持向量機(jī)的恒星光譜數(shù)據(jù)自動分類方法
發(fā)布時間：2019-03-14 21:15
【摘要】：盡管經(jīng)典的分類方法支持向量機(jī)SVM在天文學(xué)領(lǐng)域廣泛應(yīng)用,但其只考慮類間的絕對間隔而忽略類內(nèi)的分布性狀,因而分類性能有待于進(jìn)一步提升。鑒于此,提出一種新穎的基于流形判別分析和支持向量機(jī)的恒星光譜數(shù)據(jù)自動分類方法。該方法引入流形判別分析的兩個重要概念:基于流形的類內(nèi)離散度MW和基于流形的類間離散度MB。所提方法找到的分類面同時保證MW最小且MB最大。可建立相應(yīng)最優(yōu)化問題,然后將原最優(yōu)化問題轉(zhuǎn)化為QP對偶形式求得支持向量和判別函數(shù),最后利用判別函數(shù)判斷測試樣本的類屬。該方法的最大優(yōu)勢在于進(jìn)行分類決策時,不僅考慮樣本的類間信息和分布特征,而且還保持了各類的局部流形結(jié)構(gòu)。SDSS恒星光譜數(shù)據(jù)上的比較實驗表明該方法的有效性。
[Abstract]:Although the classical classification method, support Vector Machine (SVM), is widely used in astronomy, its classification performance needs to be further improved because it only considers the absolute interval between classes but neglects the intra-class distribution characteristics. In view of this, a novel classification method of star spectral data based on manifold discriminant analysis and support vector machine is proposed. In this method, two important concepts of manifold discriminant analysis are introduced: within-class dispersion MW based on manifolds and MB. between classes based on manifolds. The classification surface found by the proposed method ensures that the MW is minimum and the MB is the largest at the same time. The corresponding optimization problem can be established, then the original optimization problem is transformed into the dual form of QP to obtain the support vector and discriminant function. Finally, the classification of the test sample is judged by using the discriminant function. The greatest advantage of the proposed method is that it not only considers the inter-class information and distribution characteristics of the samples, but also maintains the local manifold structure. The comparative experiments on the star spectral data of SDSS show the effectiveness of the proposed method.
【作者單位】： 中北大學(xué)計算機(jī)與控制工程學(xué)院;中北大學(xué)信息與通信工程學(xué)院;山西大學(xué)商務(wù)學(xué)院信息學(xué)院;
【基金】：國家自然科學(xué)基金項目(61202311) 山西省高等學(xué)�？萍紕�(chuàng)新項目(20131112)資助
【分類號】：O433.4;P144.1
本文編號：2440375
[Abstract]:Although the classical classification method, support Vector Machine (SVM), is widely used in astronomy, its classification performance needs to be further improved because it only considers the absolute interval between classes but neglects the intra-class distribution characteristics. In view of this, a novel classification method of star spectral data based on manifold discriminant analysis and support vector machine is proposed. In this method, two important concepts of manifold discriminant analysis are introduced: within-class dispersion MW based on manifolds and MB. between classes based on manifolds. The classification surface found by the proposed method ensures that the MW is minimum and the MB is the largest at the same time. The corresponding optimization problem can be established, then the original optimization problem is transformed into the dual form of QP to obtain the support vector and discriminant function. Finally, the classification of the test sample is judged by using the discriminant function. The greatest advantage of the proposed method is that it not only considers the inter-class information and distribution characteristics of the samples, but also maintains the local manifold structure. The comparative experiments on the star spectral data of SDSS show the effectiveness of the proposed method.
【作者單位】： 中北大學(xué)計算機(jī)與控制工程學(xué)院;中北大學(xué)信息與通信工程學(xué)院;山西大學(xué)商務(wù)學(xué)院信息學(xué)院;
【基金】：國家自然科學(xué)基金項目(61202311) 山西省高等學(xué)�？萍紕�(chuàng)新項目(20131112)資助
【分類號】：O433.4;P144.1
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