A unified optimization framework for low-rank inducing penalties
(2020) 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition p.8471-8480- Abstract
In this paper we study the convex envelopes of a new class of functions. Using this approach, we are able to unify two important classes of regularizers from unbiased non-convex formulations and weighted nuclear norm penalties. This opens up for possibilities of combining the best of both worlds, and to leverage each method’s contribution to cases where simply enforcing one of the regularizers are insufficient. We show that the proposed regularizers can be incorporated in standard splitting schemes such as Alternating Direction Methods of Multipliers (ADMM), and other subgradient methods. Furthermore, we provide an efficient way of computing the proximal operator. Lastly, we show on real non-rigid structure-from-motion (NRSfM) datasets,... (More)
In this paper we study the convex envelopes of a new class of functions. Using this approach, we are able to unify two important classes of regularizers from unbiased non-convex formulations and weighted nuclear norm penalties. This opens up for possibilities of combining the best of both worlds, and to leverage each method’s contribution to cases where simply enforcing one of the regularizers are insufficient. We show that the proposed regularizers can be incorporated in standard splitting schemes such as Alternating Direction Methods of Multipliers (ADMM), and other subgradient methods. Furthermore, we provide an efficient way of computing the proximal operator. Lastly, we show on real non-rigid structure-from-motion (NRSfM) datasets, the issues that arise from using weighted nuclear norm penalties, and how this can be remedied using our proposed method.
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- author
- Örnhag, Marcus Valtonen LU and Olsson, Carl LU
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- series title
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- pages
- 10 pages
- conference name
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020
- conference location
- Virtual, Online, United States
- conference dates
- 2020-06-14 - 2020-06-19
- external identifiers
-
- scopus:85094558803
- ISSN
- 1063-6919
- ISBN
- 978-1-7281-7168-5
- DOI
- 10.1109/CVPR42600.2020.00850
- language
- English
- LU publication?
- yes
- id
- aead0315-05ab-4687-908e-6a581b636ad9
- date added to LUP
- 2020-11-23 09:43:07
- date last changed
- 2022-05-12 07:57:00
@inproceedings{aead0315-05ab-4687-908e-6a581b636ad9, abstract = {{<p>In this paper we study the convex envelopes of a new class of functions. Using this approach, we are able to unify two important classes of regularizers from unbiased non-convex formulations and weighted nuclear norm penalties. This opens up for possibilities of combining the best of both worlds, and to leverage each method’s contribution to cases where simply enforcing one of the regularizers are insufficient. We show that the proposed regularizers can be incorporated in standard splitting schemes such as Alternating Direction Methods of Multipliers (ADMM), and other subgradient methods. Furthermore, we provide an efficient way of computing the proximal operator. Lastly, we show on real non-rigid structure-from-motion (NRSfM) datasets, the issues that arise from using weighted nuclear norm penalties, and how this can be remedied using our proposed method.</p>}}, author = {{Örnhag, Marcus Valtonen and Olsson, Carl}}, booktitle = {{2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}}, isbn = {{978-1-7281-7168-5}}, issn = {{1063-6919}}, language = {{eng}}, pages = {{8471--8480}}, series = {{Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition}}, title = {{A unified optimization framework for low-rank inducing penalties}}, url = {{http://dx.doi.org/10.1109/CVPR42600.2020.00850}}, doi = {{10.1109/CVPR42600.2020.00850}}, year = {{2020}}, }