Journal of Frontiers of Computer Science and Technology ›› 2013, Vol. 7 ›› Issue (3): 272-281.DOI: 10.3778/j.issn.1673-9418.1205027

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Kernel-Induced Space Selection Approach to LPKHDA Dimensional Reduction Algorithm

REN Shijin1,2+, YANG Maoyun1,3, LIU Xiaoping3, XU Guiyun3   

  1. 1. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China
    2. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
    3. School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
  • Online:2013-03-01 Published:2013-03-05

诱导核空间选择的LPKHDA维数约简算法

任世锦1,2+,杨茂云1,3,刘小平3,徐桂云3   

  1. 1. 江苏师范大学 计算机学院,江苏 徐州 221116
    2. 浙江大学 工业控制技术国家重点实验室,杭州 310027
    3. 中国矿业大学 机电工程学院,江苏 徐州 221116

Abstract: Hybrid discriminant analysis (HDA) which combines principal component analysis (PCA) with linear discriminant analysis (LDA) can achieve satisfying performance for data set following complex distribution. However, HDA can not work well for complex and nonlinear distributed data. Based on manifold learning and LSSVM (least square support vector machine