A unified optimization framework for low-rank inducing penalties

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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, the issues that arise from using weighted nuclear norm penalties, and how this can be remedied using our proposed method.

Details

Authors
Organisations
External organisations
  • Chalmers University of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Computer Vision and Robotics (Autonomous Systems)
Original languageEnglish
Title of host publication2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Pages8471-8480
Number of pages10
ISBN (Electronic)978-1-7281-7168-5
Publication statusPublished - 2020
Publication categoryResearch
Peer-reviewedYes
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: 2020 Jun 142020 Jun 19

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020
CountryUnited States
CityVirtual, Online
Period2020/06/142020/06/19