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		<citationkey>FilisbinoGiraThom:2016:RaEiTh</citationkey>
		<title>Ranking Eigenfaces Through Adaboost and Perceptron Ensembles</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
		<size>416 KiB</size>
		<author>Filisbino, Tiene Andre,</author>
		<author>Giraldi, Gilson Antonio,</author>
		<author>Thomaz, Carlos Eduardo,</author>
		<affiliation>Laboratorio Nacional de Comnputação Científica</affiliation>
		<affiliation>Laboratorio Nacional de Comnputação Científica</affiliation>
		<affiliation>Centro Universitário da FEI</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>tiene@lncc.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Face Processing Application Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Ranking PCA Components, Separating Hyperplanes, Perceptron, AdaBoost, Face Image Analysis.</keywords>
		<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.</abstract>
		<language>en</language>
		<targetfile>REAPE2.pdf</targetfile>
		<usergroup>tiene@lncc.br</usergroup>
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		<citingitemlist>sid.inpe.br/sibgrapi/2016/07.02.23.50 7</citingitemlist>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2016/09.06.18.06</url>
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