Title
A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers
Abstract
Solving a linear inverse problem may include difficulties such as the presence of outliers and a mixing matrix with a large condition number. In such cases a regularized robust estimator is needed. We propose a new-type regularized robust estimator that is simultaneously highly robust against outliers, highly efficient in the presence of purely Gaussian noise, and also stable when the mixing matrix has a large condition number. We also propose an algorithm to compute the estimates, based on a regularized iterative reweighted least squares algorithm. A basic and a fast version of the algorithm are given. Finally, we test the performance of the proposed approach using numerical experiments and compare it with other estimators. Our estimator provides superior robustness, even up to 40% of outliers, while at the same time performing quite close to the optimal maximum likelihood estimator in the outlier-free case.
Year
DOI
Venue
2015
10.1109/ICASSP.2015.7178606
IEEE International Conference on Acoustics, Speech and SP
Field
DocType
ISSN
Efficient estimator,Mathematical optimization,Least trimmed squares,Computer science,Optimal estimation,Iteratively reweighted least squares,Robust statistics,Estimation theory,Linear least squares,Estimator
Conference
1520-6149
Citations 
PageRank 
References 
1
0.40
3
Authors
4
Name
Order
Citations
PageRank
Marta Martinez-Camara1101.60
Michael Muma214419.51
Abdelhak M. Zoubir31036148.03
Martin Vetterli4139262397.68