Maximum Likelihood Robust Regression by Mixture Models

Author:

Brandt Sami S.

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Geometry and Topology,Computer Vision and Pattern Recognition,Condensed Matter Physics,Modelling and Simulation,Statistics and Probability

Reference41 articles.

1. H. Akaike, “On entropy maximization principle,” in (ed.), Applications of Statistics, P.R. Krishnaiah, North Holland, Amsterdam, pp. 27–41, 1977.

2. R. Beran, “Asymptotically efficient adaptive rank estimates in location models,” Ann. Stat. Vol. 2, No. 1, pp. 63–74, 1974.

3. C. Biernacki, G. Celeux, and G. Govaert, “Strategies for getting the largest likelihood in mixture models,” Presented at the invited session in Joint Statistical Meetings 2000, Indianapolis. Presentation available at http://www.inrialpes.fr/is2/people/celeux/ , 2000.

4. S. Brandt, “Maximum likelihood robust regression with known and unknown residual models,” in Proc of the Statistical Methods in Video Proc Workshop, in conjunction with ECCV 2002, Copenhagen, Denmark, 2002a, pp. 97–102.

5. S. Brandt, “Theorems and algorithms for multiple view geometry with applications to electron tomography,” Doctoral thesis, Helsinki University of Technology, 2002b.

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1. 3-D Reconstruction of Three Views Based on Manifold Study;Communications in Computer and Information Science;2014

2. Hierarchical Similarity Transformations Between Gaussian Mixtures;IEEE Transactions on Neural Networks and Learning Systems;2013-11

3. The mixtures of Student’s t-distributions as a robust framework for rigid registration;Image and Vision Computing;2009-08

4. On the probabilistic epipolar geometry;Image and Vision Computing;2008-03

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