Robust Computation of Linear Models by Convex Relaxation-Reference-Cited by-同舟云学术

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Robust Computation of Linear Models by Convex Relaxation

Published:2014-09-23 Issue:2 Volume:15 Page:363-410
ISSN:1615-3375
Container-title:Foundations of Computational Mathematics
language:en
Short-container-title:Found Comput Math

Author:

Lerman Gilad,McCoy Michael B.,Tropp Joel A.,Zhang Teng

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Theory and Mathematics,Computational Mathematics,Analysis

Link

http://link.springer.com/content/pdf/10.1007/s10208-014-9221-0.pdf

Reference81 articles.

1. Ammann, L.P.: Robust singular value decompositions: A new approach to projection pursuit. J. Amer. Statist. Assoc. 88(422), 505–514 (1993). http://www.jstor.org/stable/2290330

2. Bargiela, A., Hartley, J.K.: Orthogonal linear regression algorithm based on augmented matrix formulation. Comput. Oper. Res. 20, 829–836 (1993). doi: 10.1016/0305-0548(93)90104-Q . http://dl.acm.org/citation.cfm?id=165819.165826

3. Basri, R., Jacobs, D.: Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003)

4. Bhatia, R.: Matrix Analysis. No. 169 in Graduate Texts in Mathematics. Springer, New York (1997)

5. Björck, Å.: Numerical Methods for Least Squares Problems. Society for Industrial and Applied Mathematics, Philadelphia, PA (1996)

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