Please use this identifier to cite or link to this item: http://www.repositorio.ufc.br/handle/riufc/4167
Title in Portuguese: Polynomial expansion of the probability density function about gaussian mixtures
Author: Cavalcante, Charles Casimiro
Mota, João César Moura
Romano, João Marcos Travassos
Keywords: Teleinformática
Issue Date: 2004
Publisher: Workshop on Machine Learning for Signal Processing
Citation: CAVALCANTE, C. C. ; MOTA, J. C. M. ; ROMANO, J. M. T. Polynomial expansion of the probability density function about gaussian mixtures. In: WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 14., 2004, São Luiz. Anais... São Luiz: IEEE, 2004. p. 163-172
Abstract: A polynomial expansion to probability density function (pdf) approximation about Gaussian mixture densities is proposed in this paper. Using known polynomial series expansions we apply the Pareen estimator to derive an orthonormal basis that is able to represent the characteristics of probability distributions that are not concentrated in the vicinity of the mean point such as the Gaussian pdf. The blind source separation problem is used to illustrate the applicability of the proposal in practical analysis of the dynamics of the recovered data pdf estimation. Simulations are carried out to illustrate the analysis.
URI: http://www.repositorio.ufc.br/handle/riufc/4167
metadata.dc.type: Outros
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