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Nonparametric Maximum Margin Criterion For Face Recognition  (Make Corrections)  
Xipeng Qiu, Lide Wu



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Abstract: Linear discriminant analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional image data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a new feature extraction method, nonparametric maximum margin criterion... (Update)

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BibTeX entry:   (Update)

@misc{ qiu-nonparametric,
  author = "Xipeng Qiu and Lide Wu",
  title = "Nonparametric Maximum Margin Criterion For Face Recognition",
  url = "citeseer.ist.psu.edu/748798.html" }
Citations (may not include all citations):
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41   Parameterisation of a stochastic model for human face identi.. - Samaria, Harter - 1994
16   Bayesian face recognition (context) - Moghaddam, Jebara et al. - 2000
11   A new LDAbased face recognition system which can solve the s.. (context) - Chen, Liao et al. - 2000
6   A direct LDA algorithm for highdimensional data with applica.. - Yu, Yang - 2067
6   Why can LDA be performed in PCA transformed space (context) - Yang, Yang - 2003
5   A generalized optimal set of discriminant vectors (context) - Liu, Cheng et al. - 1992
3   Nonparametric discriminant analysis (context) - Fukunaga, Mantock - 1983
2   Efficient and robust feature extraction by maximum margin cr.. - Li, Jiang et al. - 2003
1   Dual-space linear discriminant analysis for face recognition (context) - Wang, Tang - 2004

Documents on the same site (http://www.cs.fudan.edu.cn/mcwil/~xpqiu/Publications.html):   More
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