An adaptive greedy algorithm with application to sparse NARMA identification

Gerasimos Mileounis, Behtash Babadi, Nicholas Kalouptsidis, Vahid Tarokh
<span title="">2010</span> <i title="IEEE"> 2010 IEEE International Conference on Acoustics, Speech and Signal Processing </i> &nbsp;
Greedy algorithms form an essential tool for compressed sensing. However, their inherent batch mode discourages their use in time-varying environments due to significant complexity and storage requirements. In this paper a powerful greedy scheme developed in [1, 2] is converted into an adaptive algorithm which is applied to estimation of nonlinear channels. Performance is assessed via computer simulations on a variety of linear and nonlinear channels; all confirm significant improvements over conventional methods.
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