Martinez-Camara, Marta
Muma, Michael
Zoubir, Abdelhak M.
Vetterli, Martin
A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers
Proceedings of the 40th International Conference on Acoustics, Speech, and Signal Processing
Proceedings of the 40th International Conference on Acoustics, Speech, and Signal Processing
Proceedings of the 40th International Conference on Acoustics, Speech, and Signal Processing
Proceedings of the 40th International Conference on Acoustics, Speech, and Signal Processing
Robust statistics
linear inverse problem
2015
2015
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 tau-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.
Proceedings of the 40th International Conference on Acoustics, Speech, and Signal Processing
Conference Papers