Sparse coding with unity range codes and label consistent discriminative dictionary learning
(2017) 2016 23rd International Conference on Pattern Recognition (ICPR 2016) p.3186-3191- Abstract
A novel sparse coding framework with unity range codes and the possibility to produce a discriminative dictionary is presented. The framework is, in contrast to many other works, able to handle unsupervised, supervised and semi-supervised settings. Furthermore, codes are constrained to be in unity range, which is beneficial in many scenarios. The paper presents the framework and solvers used to produce dictionaries and codes. Experiments in image reconstruction and feature learning for classification highlight the benefits with the proposed framework.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4a2e9e1b-b1c2-4525-82ef-82c0829cd315
- author
- Nilsson, Mikael LU
- organization
- publishing date
- 2017-04-13
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2016 23rd International Conference on Pattern Recognition, ICPR 2016
- article number
- 7900125
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2016 23rd International Conference on Pattern Recognition (ICPR 2016)
- conference location
- CancĂșn, Mexico
- conference dates
- 2016-12-04 - 2016-12-08
- external identifiers
-
- scopus:85019149596
- ISBN
- 9781509048472
- DOI
- 10.1109/ICPR.2016.7900125
- language
- English
- LU publication?
- yes
- id
- 4a2e9e1b-b1c2-4525-82ef-82c0829cd315
- date added to LUP
- 2017-06-02 12:51:37
- date last changed
- 2022-01-30 20:39:00
@inproceedings{4a2e9e1b-b1c2-4525-82ef-82c0829cd315, abstract = {{<p>A novel sparse coding framework with unity range codes and the possibility to produce a discriminative dictionary is presented. The framework is, in contrast to many other works, able to handle unsupervised, supervised and semi-supervised settings. Furthermore, codes are constrained to be in unity range, which is beneficial in many scenarios. The paper presents the framework and solvers used to produce dictionaries and codes. Experiments in image reconstruction and feature learning for classification highlight the benefits with the proposed framework.</p>}}, author = {{Nilsson, Mikael}}, booktitle = {{2016 23rd International Conference on Pattern Recognition, ICPR 2016}}, isbn = {{9781509048472}}, language = {{eng}}, month = {{04}}, pages = {{3186--3191}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Sparse coding with unity range codes and label consistent discriminative dictionary learning}}, url = {{http://dx.doi.org/10.1109/ICPR.2016.7900125}}, doi = {{10.1109/ICPR.2016.7900125}}, year = {{2017}}, }