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基于特征值分解的中心支持向量機(jī)算法

陳素根,  吳小俊

陳素根, 吳小俊. 基于特征值分解的中心支持向量機(jī)算法[J]. 電子與信息學(xué)報(bào), 2016, 38(3): 557-564. doi: 10.11999/JEIT150693
引用本文: 陳素根, 吳小俊. 基于特征值分解的中心支持向量機(jī)算法[J]. 電子與信息學(xué)報(bào), 2016, 38(3): 557-564. doi: 10.11999/JEIT150693
CHEN Sugen, WU Xiaojun. Eigenvalue Proximal Support Vector Machine Algorithm Based on Eigenvalue Decoposition[J]. Journal of Electronics & Information Technology, 2016, 38(3): 557-564. doi: 10.11999/JEIT150693
Citation: CHEN Sugen, WU Xiaojun. Eigenvalue Proximal Support Vector Machine Algorithm Based on Eigenvalue Decoposition[J]. Journal of Electronics & Information Technology, 2016, 38(3): 557-564. doi: 10.11999/JEIT150693

基于特征值分解的中心支持向量機(jī)算法

doi: 10.11999/JEIT150693 cstr: 32379.14.JEIT150693
  • 2.

    (江南大學(xué)物聯(lián)網(wǎng)工程學(xué)院 無錫 214122) ②(安慶師范學(xué)院數(shù)學(xué)與計(jì)算科學(xué)學(xué)院 安慶 246133)

基金項(xiàng)目: 

國家自然科學(xué)基金(61373055, 61103128), 111引智計(jì)劃項(xiàng)目(B12018)，江蘇省工業(yè)支持計(jì)劃項(xiàng)目(BE2012031)

Eigenvalue Proximal Support Vector Machine Algorithm Based on Eigenvalue Decoposition

  • 2.

    (School of IoT Engineering, Jiangnan University, Wuxi 214122, China)

Funds: 

The National Natural Science Foundation of China (61373055, 61103128), 111 Project of Chinese Ministry of Education (B12018), Industrial Support Program of Jiangsu Province (BE2012031)

  • 摘要: 針對廣義特征值中心支持向量機(jī)(GEPSVM)訓(xùn)練和決策過程不一致問題，該文提出一類改進(jìn)的基于特征值分解的中心支持向量機(jī)，簡稱為IGEPSVM。首先針對二分類問題提出了基于特征值分解的中心支持向量機(jī)，然后基于一類對余類策略將其推廣到多類分類問題。將GEPSVM求解廣義特征值問題轉(zhuǎn)化為求解標(biāo)準(zhǔn)特征值問題，降低了計(jì)算復(fù)雜度。引入了一個(gè)新的參數(shù)，可以調(diào)節(jié)模型的性能，提高了GEPSVM的分類精度。提出了基于IGEPSVM的多類分類算法。實(shí)驗(yàn)結(jié)果表明，與GEPSVM算法相比較，IGEPSVM不僅提高了分類精度，而且縮短了訓(xùn)練時(shí)間。
    Abstract: To deal with the consistency problem of training process and decision process in Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM), an improved version of eigenvalue proximal support vector machine, called IGEPSVM for short is proposed. At first, IGEPSVM for binary classification problem is proposed, and then Multi-IGEPSVM is also presented for multi-class classification problem based on one-versus-rest strategy. The main contributions of this paper are as follows. The generalized eigenvalue decomposition problems are replaced by the standard eigenvalue decomposition problems, leading to simpler optimization problems. An extra parameter is introduced, which can adjust the performance of the model and improve the classification accuracy of GEPSVM. A corresponding multi-class classification algorithm is proposed, which is not studied in GEPSVM. Experimental results on several datasets illustrate that IGEPSVM is superior to GEPSVM in both classification accuracy and training speed.
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出版歷程
  • 收稿日期:  2015-06-08
  • 修回日期:  2015-09-17
  • 刊出日期:  2016-03-19

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