2016-08-172012-03-212012-02-10SANTANA JÚNIOR, Ewaldo éder Carvalho. BLIND SIGNAL EXTRACTION WITH TEMPORAL STRUCTURES USING HILBERT SPACE REPRODUCED BY KERNEL. 2012. 76 f. Dissertação (Mestrado em Engenharia) - Universidade Federal do Maranhão, São Luís, 2012.http://tedebc.ufma.br:8080/jspui/handle/tede/476This work derives and evaluates a nonlinear method for Blind Source Extraction (BSE) in a Reproducing Kernel Hilbert Space (RKHS) framework. For extracting the desired signal from a mixture a priori information about the autocorrelation function of that signal translated in a linear transformation of the Gram matrix of the nonlinearly transformed data to the Hilbert space. Our method proved to be more robust than methods presented in the literature of BSE with respect to ambiguities in the available a priori information of the signal to be extracted. The approach here introduced can also be seen as a generalization of Kernel Principal Component Analysis to analyze autocorrelation matrices at specific time lags. Henceforth, the method here presented is a kernelization of Dependent Component Analysis, it will be called Kernel Dependent Component Analysis (KDCA). Also in this dissertation it will be show a Information-Theoretic Learning perspective of the analysis, this will study the transformations in the extracted signals probability density functions while linear operations calculated in the RKHS.application/pdfAcesso AbertoExtração Cega de FontesEspaço de Hilbert Reproduzido por KernelAprendizagem de Máquina utilizando Teoria da InformaçãoBlind Signal ExtractionReproducing Kernel Hilbert SpacesInformation-Theoretic LearningCNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO::PESQUISA OPERACIONAL::PROGRAMACAO LINEAR, NAO-LINEAR, MISTA E DINAMICADissertação