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基于大间距准则的不相关保局投影分析

龚劬 唐萍峰

龚劬, 唐萍峰. 基于大间距准则的不相关保局投影分析. 自动化学报, 2013, 39(9): 1575-1580. doi: 10.3724/SP.J.1004.2013.01575
引用本文: 龚劬, 唐萍峰. 基于大间距准则的不相关保局投影分析. 自动化学报, 2013, 39(9): 1575-1580. doi: 10.3724/SP.J.1004.2013.01575
GONG Qu, TANG Ping-Feng. Uncorrelated Locality Preserving Projections Analysis Based on Maximum Margin Criterion. ACTA AUTOMATICA SINICA, 2013, 39(9): 1575-1580. doi: 10.3724/SP.J.1004.2013.01575
Citation: GONG Qu, TANG Ping-Feng. Uncorrelated Locality Preserving Projections Analysis Based on Maximum Margin Criterion. ACTA AUTOMATICA SINICA, 2013, 39(9): 1575-1580. doi: 10.3724/SP.J.1004.2013.01575

基于大间距准则的不相关保局投影分析

doi: 10.3724/SP.J.1004.2013.01575
基金项目: 

重庆大学"211 工程"三期创新人才培养计划建设项目(S-09110)资助

详细信息
    作者简介:

    龚劬 重庆大学数学与统计学院教授.主要研究方向为数字图像处理和小波分析. E-mail: gong_qu@163.com

Uncorrelated Locality Preserving Projections Analysis Based on Maximum Margin Criterion

Funds: 

Supported by Innovative Talent Training Project, the Third Stage of "211 Project", Chongqing University (S-09110)

  • 摘要: 局部保持投影(Locality preserving projections,LPP)算法只保持了目标在投影后的邻域局部信息,为了更好地刻画数据的流形结构, 引入了类内和类间局部散度矩阵,给出了一种基于有效且稳定的大间距准则(Maximum margin criterion,MMC)的不相关保局投影分析方法.该方法在最大化散度矩阵迹差时,引入尺度因子α,对类内和类间局部散度矩阵进行加权,以便找到更适合分类的子空间并且可避免小样本问题; 更重要的是,大间距准则下提取的判别特征集一般情况下是统计相关的,造成了特征信息的冗余, 因此,通过增加一个不相关约束条件,利用推导出的公式提取不相关判别特征集, 这样做, 对正确识别更为有利.在Yale人脸库、PIE人脸库和MNIST手写数字库上的测试结果表明,本文方法有效且稳定, 与LPP、LDA (Linear discriminant analysis)和LPMIP(Locality-preserved maximum information projection)方法等相比,具有更高的正确识别率.
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出版历程
  • 收稿日期:  2012-06-26
  • 修回日期:  2013-01-05
  • 刊出日期:  2013-09-20

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