Title :
Robust joint sparse recovery on data with outliers
Author :
Balkan, Ozgur ; Kreutz-Delgado, Kenneth ; Makeig, Scott
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of California San Diego, La Jolla, CA, USA
Abstract :
We propose a method to solve the multiple measurement vector (MMV) sparse signal recovery problem in a robust manner when data contains outlier points which do not fit the shared sparsity structure otherwise contained in the data. This scenario occurs frequently in the applications of MMV models due to only partially known source dynamics. The algorithm we propose is a modification of MMV-based sparse bayesian learning (M-SBL) by incorporating the idea of least trimmed squares (LTS), which has previously been developed for robust linear regression. Experiments show a significant performance improvement over the conventional M-SBL under different outlier ratios and amplitudes.
Keywords :
compressed sensing; least mean squares methods; regression analysis; least trimmed squares; multiple measurement vector; outlier points; robust joint sparse recovery; robust linear regression; source dynamics; sparse Bayesian learning; sparse signal recovery problem; Bayes methods; Cost function; Joints; Linear regression; Noise; Robustness; Vectors; Joint Sparse Signal Recovery; Least Trimmed Squares; Robust Statistics; Sparse Bayesian Learning;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6638373