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Seminário: Feature LMS Algorithms: Exploiting Hidden Sparsity
Segunda-feira 04 Fevereiro 2019, 11:00 - 13:00
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We have been witnessing a growing research activity to advance new strategies to detect and exploit underlying sparsity in the parameters of physical models. In many cases, the sparsity is not explicit in the relations among the parameters coefficients requiring some suitable tools to reveal the potential sparsity. In this presentation, we will propose a family of LMS-type algorithms, collectively named Feature LMS (F-LMS) algorithms, aimed at exposing some hidden features of the unknown parameters. As a byproduct, the new algorithms will increase the convergence speed and reduce the steady-state mean-squared error, in comparison with the classical LMS solution. The main idea is to apply linear transformations, by means of the so-called feature matrices, to reveal the sparsity hidden in the coefficient vector, followed by a sparsity-promoting penalty function to exploit the exposed sparsity. For illustration, a few F-LMS algorithms for lowpass, highpass, and bandpass systems are also introduced by using simple feature matrices that require only trivial operations. Simulation results and real-life experiments demonstrate that the proposed F-LMS algorithms bring about several performance improvements whenever the unknown sparsity of the parameters is exposed.

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