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近邻类鉴别分析方法

, PP. 406-410

Keywords: 线性鉴别分析(LDA),近邻类鉴别分析(NCLDA),手写汉字识别

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Abstract:

提出一种近邻类鉴别分析方法,线性鉴别分析是该方法的一个特例。线性鉴别分析通过最大化类间散度同时最小化类内散度寻找最佳投影,其中类间散度是所有类之间散度的总体平均;而近邻类鉴别分析中类间散度定义为各个类与其k个近邻类之间的平均散度。该方法通过选取适当的近邻类数,能够缓解线性鉴别降维后造成的部分类的重叠。实验结果表明近邻类鉴别分析方法性能稳定且优于传统的线性鉴别分析。

References

[1]  Liu Chenglin.Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition.IEEE Trans on Pattern Analysis and Machine Intelligence,2007,29(8): 1465-1469
[2]  Fisher R A.The Statistical Utilization of Multiple Measurements.Annals of Eugenics,1938,8(4): 376-386
[3]  Rao C R.The Utilization of Multiple Measurements in Problems of Biological Classification.Journal of Royal Statistical: Society B,1948,10(2): 159-203
[4]  Loog M,Duin R,Haeb-Umbach R.Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria.IEEE Trans on Pattern Analysis and Machine Intelligence,2001,23(7): 762-766
[5]  Li Zhifeng,Lin Dahua,Tang Xiaoou.Nonparametric Discriminant Analysis for Face Recognition.IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(4): 755-761
[6]  Tao Dacheng,Li Xuelong,Wu Xindong,et al.Geometric Mean for Subspace Selection.IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(2): 260-274
[7]  Liu Chengjun,Wechsler H.Enhanced Fisher Linear Discriminant Models for Face Recognition // Proc of the 14th International Conference on Pattern Recognition.Brisbane,Australia,1998,II: 1368-1372
[8]  Yu Hua,Yang Jie.A Direct LDA Algorithm for High-Dimensional Data with Application to Face Recognition.Pattern Recognition,2001,34(10): 2067-2070
[9]  Fukunaga K.Introduction to Statistical Pattern Recognition.London,UK: Academic Press,1990
[10]  Bressan M,Vitria J.Nonparametric Discriminant Analysis and Nearest Neighbor Classification.Pattern Recognition Letters,2003,24(15): 2743-2749
[11]  Li Zhifeng,Lin Dahua,Tang Xiaoou.Nonparametric Subspace Analysis for Face Recognition // Proc of the International Conference on Computer Vision and Pattern Recognition.San Diego,USA,2005: 961-966
[12]  Rueda L,Oommen B J,Henriquez C.Multi-Class Pairwise Linear Dimensionality Reduction Using Heteroscedastic Schemes.Pattern Recognition,2010,43(7): 2456-2465
[13]  Wang Haixian,Chen Sibao,Hu Zilan,et al.Locality-Preserved Maximum Information Projection.IEEE Trans on Neural Networks,2008,19(4): 571-585
[14]  Roweis S T,Saul L K.Nonlinear Dimensionality Reduction by Locally Linear Embedding.Science,2000,290(5500): 2323-2326
[15]  Gao Tianfu,Liu Chenglin.High Accuracy Handwritten Chinese Character Recognition Using LDA-Based Compound Distances.Pattern Recognition,2008,41(11): 3442-3451
[16]  Kimura F,Takashina K,Tsuruoka S,et al.Modified Quadratic Discriminant Function and the Application to Chinese Character Recognition.IEEE Trans on Pattern Analysis and Machine Intelligence,1987,9(1): 149-153
[17]  Xu Bo,Huang Kaizhu,Liu Chenglin.Dimensionality Reduction by Minimal Distance Maximization // Proc of the 20th International Conference on Pattern Recognition.Istanbul,Turkey,2010: 569-572

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