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Data-Selective Learning for Sparse System Identification
May 26, 2016 @ 5:00 pm - 6:30 pm EDT
This talk addresses fundamental as well as some advanced concepts that are important to understand the principles of data-selective algorithms in sparse system identification. This type of algorithms utilize environmental data for updating system parameters only when they bring new information. As a result, the obtained parameter estimations become more accurate without sacrificing the learning speed. The data-selective algorithms are particularly suitable for applications where computational resources are limited and/or power saving is a requirement.
Two adaptive filtering algorithms that combine a sparsity-promoting scheme with a data-selection mechanism are presented. Sparsity is promoted via approximations to the l0 norm in order to increase convergence speed of the algorithms when dealing with sparse/compressible signals. These approximations circumvent some difficulties of working with the l0 norm, thus allowing the development of online algorithms. Data selection is implemented based on set-membership filtering, which yields robustness against noise and reduced computational burden. These algorithms are analyzed in order to set properly their parameters to guarantee stability. In addition, we characterize their updating processes from a geometrical viewpoint. Simulation results show that the new algorithms outperform the state-of-the-art algorithms designed to exploit sparsity.
Speaker(s): PAULO S. R. DINIZ,
Room: EV 003.309
Bldg: Concordia University, EV Building
Montreal, Rio de Janeiro