We present a nonparametric algorithm for finding localized energy solutions from limited
data. The problem we address is underdetermined, and no prior knowledge of the shape of …

Sparse Bayesian learning (SBL) and specifically relevance vector machines have received
much attention in the machine learning literature as a means of achieving parsimonious …

We address the problem of finding sparse solutions to an underdetermined system of
equations when there are multiple measurement vectors having the same, but unknown …

A methodology is developed to derive algorithms for optimal basis selection by minimizing
diversity measures proposed by Wickerhauser (1994) and Donoho (1994). These measures …

The paper describes a new algorithm for tomographic source reconstruction in neural
electromagnetic inverse problems. Termed FOCUSS (FOCal Underdetermined System …

In seismic data processing, we often need to interpolate and extrapolate data at missing
spatial locations. The reconstruction problem can be posed as an inverse problem where …

We present an iterative nonparametric approach to spectral estimation that is particularly
suitable for estimation of line spectra. This approach minimizes a cost function derived from …

Compressive sensing (CS) theory indicates that the optimal reconstruction of an unknown
sparse signal can be achieved from limited noisy measurements by solving a sparsity-driven …

With regard to the multifunction radar transmitting sparse stepped-frequency-modulation
(SSFM) signal for inverse synthetic aperture radar (ISAR) imaging, the received echo signal …

The problem of signal representation in terms of basis vectors from a large, overcomplete,
spanning dictionary has been the focus of much research. Achieving a succinct, orsparse' …