Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements

Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on...

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Bibliographische Detailangaben

Hauptverfasser:	Carrillo, R.E., Barner, K.E.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Compressed sensing Computational complexity Computational modeling Equations Iterative methods Least squares approximation Least squares methods re-weighted least squares sampling methods Signal processing Signal reconstruction Sparse matrices Statistics underdetermined systems of linear equations
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