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一种新型多核判别分析方法

梁军,  张飞云,  陈龙,  李世浩,  顾胜强,  张婉婉

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文章导航 > 西南交通大学学报  > 2015 >  28(6): 1122-1129.
梁军, 张飞云, 陈龙, 李世浩, 顾胜强, 张婉婉. 一种新型多核判别分析方法[J]. 西南交通大学学报, 2015, 28(6): 1122-1129. doi: 10.3969/j.issn.0258-2724.2015.06.021
引用本文: 梁军, 张飞云, 陈龙, 李世浩, 顾胜强, 张婉婉. 一种新型多核判别分析方法[J]. 西南交通大学学报, 2015, 28(6): 1122-1129. doi: 10.3969/j.issn.0258-2724.2015.06.021
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LIANG Jun, ZHANG Feiyun, CHEN Long, LI Shihao, GU Shengqiang, ZHANG Wanwan. A New Multi-kernel Discriminant Analysis[J]. Journal of Southwest Jiaotong University, 2015, 28(6): 1122-1129. doi: 10.3969/j.issn.0258-2724.2015.06.021
Citation: LIANG Jun, ZHANG Feiyun, CHEN Long, LI Shihao, GU Shengqiang, ZHANG Wanwan. A New Multi-kernel Discriminant Analysis[J]. Journal of Southwest Jiaotong University, 2015, 28(6): 1122-1129. doi: 10.3969/j.issn.0258-2724.2015.06.021
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一种新型多核判别分析方法

doi: 10.3969/j.issn.0258-2724.2015.06.021
基金项目: 

国家自然科学基金资助项目(61573171,51108209,61203244)

交通运输部信息化项目(2013-364-836-900)

江苏高校优势学科建设工程资助项目(PAPD)

全国统计科学研究项目(2014596)

江苏省自然科学基金资助项目(BK20140570)

江苏省六大人才高峰资助项目(DZXX-048)

详细信息
    作者简介:

    梁军(1976-),男,副教授,研究方向为智能交通理论及应用,电话:0511-88782845,E-mail:liangjun@ujs.edu.cn

A New Multi-kernel Discriminant Analysis

    摘要
  • 摘要: 为了给模式分类和维度约简提供有效的手段,在综合L1-MKDA和L2-MKDA两种多核KDA优点的基础上,提出一种以预定内核函数的线性组合,并结合混合范数正则化函数实现核权重的稀疏性和非稀疏性平衡的新型弹性多核判别分析方法(semi-infinite-programming-based flexible multi-kernel discriminant analysis, S-FMKDA).该方法用半无限规划方法求解弹性多核判别分析算法,并通过混合正则化实现核的自学习.在不同数据集上的实验结果表明:S-FMKDA比目前常见的KDA、KDAP、KDAG、L1-MKDA、L2-MKDA、UMKDA核判别分析方法的精度提高5%.

     

    Abstract: In order to provide effective means for pattern classification and dimension reduction and stem from the advantages of two kinds of multi-kernel namely L1-MKDA and L2-MKDA, a new type of semi-infinite-programming-based flexible multi kernel discriminant analysis method was proposed, which is based on a linear combination of the predefined kernel function, and can utilize mixed norm regularization function to balance the sparsity of kernel weights. It applys semi-infinite programming algorithm to solve the elastic multi-core discriminant analysis, and achieves nuclear self-learning through the mixed regularization. Finally, the experimental results for different data sets demonstrate that the accuracy of the proposed algorithm is 5% better than those of KDA、KDAP、KDAG、L1-MKDA、L2-MKDA and UMKDA.

     

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出版历程
  • 收稿日期:  2014-07-23
  • 刊出日期:  2015-12-25

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