@inproceedings{c869ee7aad63468c8b4e629729a35f1f,
title = "L1-penalized robust estimation for a class of inverse problems arising in multiview geometry",
abstract = "We propose a new approach to the problem of robust estimation in multiview geometry. Inspired by recent advances in the sparse recovery problem of statistics, we define our estimator as a Bayesian maximum a posteriori with multivariate Laplace prior on the vector describing the outliers. This leads to an estimator in which the fidelity to the data is measured by the L ∞-norm while the regularization is done by the L 1-norm. The proposed procedure is fairly fast since the outlier removal is done by solving one linear program (LP). An important difference compared to existing algorithms is that for our estimator it is not necessary to specify neither the number nor the proportion of the outliers. We present strong theoretical results assessing the accuracy of our procedure, as well as a numerical example illustrating its efficiency on real data.",
author = "Dalalyan, \{Arnak S.\} and Renaud Keriven",
year = "2009",
month = jan,
day = "1",
language = "English",
isbn = "9781615679119",
series = "Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference",
publisher = "Neural Information Processing Systems",
pages = "441--449",
booktitle = "Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference",
note = "23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 ; Conference date: 07-12-2009 Through 10-12-2009",
}