Proceedings PaperAdaptive signal estimation using projection libraries
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Many results in approximation theory, nonparametric regression, and adaptive signal representation assume that the signal is a smooth function or nonstationary in some smooth sense. These assumptions are not always applicable and perhaps even more importantly the times where the signal is not smooth are themselves important features to preserve in the signal representation. Previously, computationally efficient methods were developed to implement these approaches for the relatively simple problem of estimating multiple change points from a piecewise constant signal. In this paper, this approach is presented in the modern framework of waveform dictionaries, bases libraries, and atomic decomposition.