000203430 001__ 203430
000203430 005__ 20180317094144.0
000203430 037__ $$aCONF
000203430 245__ $$aA new robust and efficient estimator for ill-conditioned linear inverse problems with outliers
000203430 269__ $$a2015
000203430 260__ $$c2015
000203430 336__ $$aConference Papers
000203430 520__ $$aSolving 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.
000203430 6531_ $$aRobust statistics
000203430 6531_ $$alinear inverse problem
000203430 700__ $$0246438$$aMartinez-Camara, Marta$$g215195
000203430 700__ $$aMuma, Michael
000203430 700__ $$aZoubir, Abdelhak M.
000203430 700__ $$0240184$$aVetterli, Martin$$g107537
000203430 7112_ $$aInternational Conference on Acoustics, Speech, and Signal Processing$$cBrisbane, Australia$$d2015
000203430 773__ $$q3422-3426$$tProceedings of the 40th International Conference on Acoustics, Speech, and Signal Processing
000203430 8564_ $$uhttps://github.com/LCAV/RegularizedTauEstimator$$zURL
000203430 8564_ $$s877485$$uhttps://infoscience.epfl.ch/record/203430/files/_presentation.pdf
000203430 8564_ $$s223503$$uhttps://infoscience.epfl.ch/record/203430/files/martinez-camara.pdf$$yPublisher's version$$zPublisher's version
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000203430 937__ $$aEPFL-CONF-203430
000203430 973__ $$aEPFL$$rNON-REVIEWED$$sPUBLISHED
000203430 980__ $$aCONF