Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC

Magnus Oskarsson, Kenneth Batstone, Kalle Astrom

CVPR 2016

doi:10.1109/CVPR.2016.627 /cvpr/2016/oskarsson2016cvpr-trust/

Abstract

In this paper we present a system for performing low rank matrix factorization. Low-rank matrix factorization is an essential problem in many areas including computer vision, with applications in e.g. affine structure-from-motion, photometric stereo, and non-rigid structure from motion. We specifically target structured data patterns, with outliers and large amounts of missing data. Using recently developed characterizations of minimal solutions to matrix factorization problems with missing data, we show how these can be used as building blocks in a hierarchical system that performs bootstrapping on all levels. This gives an robust and fast system, with state-of-the-art performance.

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Cite

Text

Oskarsson et al. "Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC." Conference on Computer Vision and Pattern Recognition, 2016. doi:10.1109/CVPR.2016.627

Markdown

[Oskarsson et al. "Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/oskarsson2016cvpr-trust/) doi:10.1109/CVPR.2016.627

BibTeX

@inproceedings{oskarsson2016cvpr-trust,
  title     = {{Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC}},
  author    = {Oskarsson, Magnus and Batstone, Kenneth and Astrom, Kalle},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2016},
  doi       = {10.1109/CVPR.2016.627},
  url       = {https://mlanthology.org/cvpr/2016/oskarsson2016cvpr-trust/}
}