Convex Envelopes for Low Rank Approximation
(2015) 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2015 8932. p.1-14- Abstract
- In this paper we consider the classical problem of finding a low rank approximation of a given matrix. In a least squares sense a closed form solution is available via factorization. However, with additional constraints, or in the presence of missing data, the problem becomes much more difficult. In this paper we show how to efficiently compute the convex envelopes of a class of rank minimization formulations. This opens up the possibility of adding additional convex constraints and functions to the minimization problem resulting in strong convex relaxations. We evaluate the framework on both real and synthetic data sets and demonstrate state-of-the-art performance.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7790959
- author
- Larsson, Viktor LU and Olsson, Carl LU
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015
- volume
- 8932
- pages
- 1 - 14
- publisher
- Springer
- conference name
- 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2015
- conference location
- Hong Kong, China
- conference dates
- 2015-01-13 - 2015-01-16
- external identifiers
-
- wos:000357502000001
- scopus:84921849025
- ISSN
- 1611-3349
- 0302-9743
- language
- English
- LU publication?
- yes
- id
- b1b09423-fea4-4528-aa22-3e1f67c1f643 (old id 7790959)
- alternative location
- http://link.springer.com/chapter/10.1007%2F978-3-319-14612-6_1
- date added to LUP
- 2016-04-01 10:21:00
- date last changed
- 2024-11-04 05:13:55
@inproceedings{b1b09423-fea4-4528-aa22-3e1f67c1f643,
abstract = {{In this paper we consider the classical problem of finding a low rank approximation of a given matrix. In a least squares sense a closed form solution is available via factorization. However, with additional constraints, or in the presence of missing data, the problem becomes much more difficult. In this paper we show how to efficiently compute the convex envelopes of a class of rank minimization formulations. This opens up the possibility of adding additional convex constraints and functions to the minimization problem resulting in strong convex relaxations. We evaluate the framework on both real and synthetic data sets and demonstrate state-of-the-art performance.}},
author = {{Larsson, Viktor and Olsson, Carl}},
booktitle = {{Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015}},
issn = {{1611-3349}},
language = {{eng}},
pages = {{1--14}},
publisher = {{Springer}},
title = {{Convex Envelopes for Low Rank Approximation}},
url = {{http://link.springer.com/chapter/10.1007%2F978-3-319-14612-6_1}},
volume = {{8932}},
year = {{2015}},
}