@InProceedings{FilisbinoGiraThom:2016:RaEiTh,
               author = "Filisbino, Tiene Andre and Giraldi, Gilson Antonio and Thomaz, 
                         Carlos Eduardo",
          affiliation = "{Laboratorio Nacional de Comnputa{\c{c}}{\~a}o 
                         Cient{\'{\i}}fica} and {Laboratorio Nacional de 
                         Comnputa{\c{c}}{\~a}o Cient{\'{\i}}fica} and {Centro 
                         Universit{\'a}rio da FEI}",
                title = "Ranking Eigenfaces Through Adaboost and Perceptron Ensembles",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Ranking PCA Components, Separating Hyperplanes, Perceptron, 
                         AdaBoost, Face Image Analysis.",
             abstract = "The fact that principal component analysis (PCA) does not 
                         necessarily represent important discriminant directions to 
                         separate sample groups motivates the development of the 
                         multi-class discriminant principal component analysis (MDPCA). 
                         This technique addresses the problem of ranking face features in 
                         N-class problems computed by PCA components (eigenfaces). Given a 
                         database, the MDPCA builds a linear support vector machine (SVM) 
                         ensemble to get the separating hyperplanes that are combined 
                         through an AdaBoost technique to determine the discriminant 
                         contribution of each PCA feature. In this paper, we follow the 
                         MDPCA methodology but we replace the SVM by the linear perceptron 
                         as the basic learner in the AdaBoost approach. In the 
                         computational experiments we compare the obtained technique, 
                         called MDPCA-Perceptron, with the PCA and the original MDPCA 
                         through facial expression experiments. Our computational results 
                         have shown that the principal components selected by the 
                         MDPCA-Perceptron allow competitive recognition rates in lower 
                         dimensional spaces with promising results for reconstruction tasks 
                         as well.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3MD4NJB",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3MD4NJB",
           targetfile = "REAPE2.pdf",
        urlaccessdate = "2024, Apr. 28"
}