@InProceedings{FilisbinoGiraThom:2016:RaPrCo,
               author = "Filisbino, Tiene Andre and Giraldi, Gilson Antonio and Thomaz, 
                         Carlos Eduardo",
          affiliation = "{National Laboratory for Scientific Computing} and {National 
                         Laboratory for Scientific Computing} and {Department of Electrical 
                         Engineering - FEI}",
                title = "Ranking Principal Components in Face Spaces Through AdaBoost.M2 
                         Linear Ensemble",
            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 = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "Discriminant Analysis, Principal Component Analysis, Support 
                         Vector Machine, Ensemble Methods, AdaBoost.",
             abstract = "Despite the success of Principal Component Analysis (PCA) for 
                         dimensionality reduction, it is known that its most expressive 
                         components do not necessarily represent important discriminant 
                         features for pattern recognition. In this paper, the problem of 
                         ranking PCA components, computed from multi-class databases, is 
                         addressed by building multiple linear learners that are combined 
                         through the AdaBoost.M2 in order to determine the discriminant 
                         contribution of each PCA feature. In our implementation, each 
                         learner is a weakened version of a linear support vector machine 
                         (SVM). The strong learner built by the ensemble technique is 
                         processed following a strategy to get the global discriminant 
                         vector to sort PCA components according to their relevance for 
                         classification tasks. Also, we show how the proposed methodology 
                         to compute the global discriminant vector can be applied to other 
                         multi-class approaches, like the linear discriminant analysis 
                         (LDA). In the computational experiments we compare the obtained 
                         approaches with counterpart ones using facial expression 
                         experiments. Our experimental results have shown that the 
                         principal components selected by the proposed technique allows 
                         higher recognition rates using less linear features.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.056",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.056",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M3R5NP",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M3R5NP",
           targetfile = "PID4355033.pdf",
        urlaccessdate = "2025, Nov. 09"
}