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Blind Source Separation Using Variational Expectation-Maximization Algorithm

Authors: Nikolaos Nasios; Adrian G. Bors;

Blind Source Separation Using Variational Expectation-Maximization Algorithm

Abstract

In this paper we suggest a new variational Bayesian approach. Variational Expectation-Maximization (VEM) algorithm is proposed in order to estimate a set of hyperparameters modelling distributions of parameters characterizing mixtures of Gaussians. We consider maximum log-likelihood (ML) estimation for the initialization of the hyperparameters. The ML estimation is employed on distributions of parameters obtained from successive runs of the EM algorithm on the same data set. The proposed algorithm is used for unsupervised detection of quadrature amplitude and phase-shift-key modulated signals.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Average
Top 10%
Average
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