Zhang, Y and Kingsbury, N (2010) Fast L0-based sparse signal recovery. Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010. pp. 403-408.Full text not available from this repository.
This paper develops an algorithm for finding sparse signals from limited observations of a linear system. We assume an adaptive Gaussian model for sparse signals. This model results in a least square problem with an iteratively reweighted L2 penalty that approximates the L0-norm. We propose a fast algorithm to solve the problem within a continuation framework. In our examples, we show that the correct sparsity map and sparsity level are gradually learnt during the iterations even when the number of observations is reduced, or when observation noise is present. In addition, with the help of sophisticated interscale signal models, the algorithm is able to recover signals to a better accuracy and with reduced number of observations than typical L1-norm and reweighted L1 norm methods. ©2010 IEEE.
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:13|
|Last Modified:||22 Feb 2017 00:20|