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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.
  • [1] Jain A K, Duin R P W, Mao J C. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(1): 4-37
    [2] Liu Qing-Shan, Lu Han-Qing, Ma Song-De. A survey: subspace analysis for face recognition. Acta Automatica Sinica, 2003, 29(6): 900-911(刘青山, 卢汉清, 马颂德. 综述人脸识别中的子空间方法. 自动化学报, 2003, 29(6): 900-911)
    [3] Gao Jun, Sun Chang-Yin, Wang Shi-Tong. (2D) 2UFFCA: two-directional two-dimensional unsupervised feature extraction method with fuzzy clustering ability. Acta Automatica Sinica, 2012, 38(4): 549-562(皋军, 孙长银, 王士同. 具有模糊聚类功能的双向二维无监督特征提取方法. 自动化学报, 2012, 38(4): 549-562)
    [4] Wang R P, Shan S G, Chen X L, Dai Q H, Gao W. Manifold-manifold distance and its application to face recognition with image sets. IEEE Transactions on Image Processing, 2012, 21(10): 4466-4479
    [5] Jolliffe I T. Principal Component Analysis. Berlin: Springer-Verlag, 2002
    [6] Fisher R A. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 1936, 7(2): 179-188
    [7] Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720
    [8] Li H F, Jiang T, Zhang K S. Efficient and robust feature extraction by maximum margin criterion. IEEE Transactions on Neural Networks, 2006, 17(1): 157-165
    [9] Lu J W, Tan Y P. Regularized locality preserving projections and its extensions for face recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40(3): 958-963
    [10] Zheng Jian-Wei, Wang Wan-Liang, Yao Xiao-Ming, Shi Hai-Yan. Face recognition using tensor local Fisher discriminant analysis. Acta Automatica Sinica, 2012, 38(9): 1485-1495(郑建炜, 王万良, 姚晓敏, 石海燕. 张量局部Fisher判别分析的人脸识别. 自动化学报, 2012, 38(9): 1485-1495)
    [11] He X F, Niyogi P. Locality preserving projections. In: Proceedings of the 16th Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2003. 153-160
    [12] Yan S C, Xu D, Zhang B Y, Zhang H J. Graph embedding: a general framework for dimensionality reduction. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 830-837
    [13] Yang J, Yang J Y, Zhang D. What's wrong with Fisher criterion? Pattern Recognition, 2002, 35(11): 2665-2668
    [14] Zhang Z Y, Wang J, Zha H Y. Adaptive manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(2): 253-265
    [15] Jin Z, Yang J Y, Hu Z S, Lou Z. Face recognition based on the uncorrelated discriminant transformation. Pattern Recognition, 2001, 34(7): 1405-1416
    [16] Wang H X, Chen S B, Hu Z L, Zheng W M. Locality-preserved maximum information projection. IEEE Transactions on Neural Networks, 2008, 19(4): 571-585
    [17] Lin Yu-E, Gu Guo-Chang, Liu Hai-Bo, Shen Jing. A recognition method using uncorrelated discriminant locality preserving projections. Journal of Harbin Engineering University. 2010, 31(1): 98-101, 114(林玉娥, 顾国昌, 刘海波, 沈晶. 不相关局部保持鉴别分析算法. 哈尔滨工程大学学报, 2010, 31(1): 98-101, 114)
    [18] Lin Yu-E, Gu Guo-Chang, Liu Hai-Bo, Shen Jing, Zhao Jing. An orthogonal feature extraction method based on the within-class preserving for small sample size problem. Acta Automatica Sinica, 2010, 36(5): 644-649(林玉娥, 顾国昌, 刘海波, 沈晶, 赵靖. 适用于小样本问题的具有类内保持的正交特征提取算法. 自动化学报, 2010, 36(5): 644-649)
    [19] Liu Q S, Tang X O, Lu H Q, Ma S D. Face recognition using kernel scatter-difference-based discriminant analysis. IEEE Transactions on Neural Networks, 2006, 17(4): 1081-1085
    [20] Li W, Prasad S, Fowler J E, Bruce L M. Locality-preserving discriminant analysis in kernel-induced feature spaces for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2011, 8(5): 894-898
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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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