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A Convex Approach to Low Rank Matrix Approximation with Missing Data

Olsson, Carl LU and Oskarsson, Magnus LU (2009) 16th Scandinavian Conference on Image Analysis In Image Analysis, Proceedings 5575. p.301-309
Abstract
Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problem is that they can be efficiently solved using singular value decomposition. However this approach fails if the measurement matrix contains missing data. In this paper we propose a new method for estimating missing data. Our approach is similar to that of L-1 approximation schemes that have been successfully used for recovering sparse solutions of under-determined linear systems. We use the nuclear norm to formulate a convex approximation of the missing data problem. The method has been tested on real and synthetic images with promising results.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Image Analysis, Proceedings
volume
5575
pages
301 - 309
publisher
Springer
conference name
16th Scandinavian Conference on Image Analysis
external identifiers
  • wos:000268661000031
  • scopus:70350650414
ISSN
1611-3349
0302-9743
DOI
10.1007/978-3-642-02230-2_31
language
English
LU publication?
yes
id
525ceb5f-4e76-4c70-b704-219d435ce975 (old id 1460145)
date added to LUP
2009-08-17 14:39:38
date last changed
2017-07-30 03:31:03
@inproceedings{525ceb5f-4e76-4c70-b704-219d435ce975,
  abstract     = {Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problem is that they can be efficiently solved using singular value decomposition. However this approach fails if the measurement matrix contains missing data. In this paper we propose a new method for estimating missing data. Our approach is similar to that of L-1 approximation schemes that have been successfully used for recovering sparse solutions of under-determined linear systems. We use the nuclear norm to formulate a convex approximation of the missing data problem. The method has been tested on real and synthetic images with promising results.},
  author       = {Olsson, Carl and Oskarsson, Magnus},
  booktitle    = {Image Analysis, Proceedings},
  issn         = {1611-3349},
  language     = {eng},
  pages        = {301--309},
  publisher    = {Springer},
  title        = {A Convex Approach to Low Rank Matrix Approximation with Missing Data},
  url          = {http://dx.doi.org/10.1007/978-3-642-02230-2_31},
  volume       = {5575},
  year         = {2009},
}