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@InProceedings{ThomazGill:2005:ApFaRe,
               author = "Thomaz, Carlos Eduardo and Gillies, Duncan Fyfe",
          affiliation = "Centro Universitario da FEI, Sao Paulo, Brazil and Imperial 
                         College, London, UK",
                title = "A maximum uncertainty LDA-based approach for limited sample size 
                         problems - with application to face recognition",
            booktitle = "Proceedings...",
                 year = "2005",
               editor = "Rodrigues, Maria Andr?ia Formico and Frery, Alejandro C?sar",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 18. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "LDA, maximum uncertainty LDA, limited sample size, face 
                         recognition.",
             abstract = "A critical issue of applying Linear Discriminant Analysis (LDA) is 
                         both the singularity and instability of the within-class scatter 
                         matrix. In practice, particularly in image recognition 
                         applications such as face recognition, there are often a large 
                         number of pixels or pre-processed features available, but the 
                         total number of training patterns is limited and commonly less 
                         than the dimension of the feature space. In this paper, a maximum 
                         uncertainty LDA-based method is proposed. It is based on a 
                         straightforward stabilisation approach for the within-class 
                         scatter matrix. In order to evaluate its effectiveness, 
                         experiments on face recognition using the well-known ORL and FERET 
                         face databases were carried out and compared with other LDA-based 
                         methods. The results indicate that our method improves the LDA 
                         classification performance when the within-class scatter matrix is 
                         not only singular but also poorly estimated, with or without a 
                         Principal Component Analysis intermediate step and using less 
                         linear discriminant features.",
  conference-location = "Natal",
      conference-year = "9-12 Oct. 2005",
             language = "en",
           targetfile = "thomazc_mlda.pdf",
        urlaccessdate = "2020, Dec. 05"
}


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