Joint learning and dictionary construction for pattern recognition
We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the repres...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/7656 |
| _version_ | 1848745432628330496 |
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| author | Pham, DucSon Venkatesh, Svetha |
| author2 | Not known |
| author_facet | Not known Pham, DucSon Venkatesh, Svetha |
| author_sort | Pham, DucSon |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small. |
| first_indexed | 2025-11-14T06:17:16Z |
| format | Conference Paper |
| id | curtin-20.500.11937-7656 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:17:16Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-76562017-09-13T14:35:41Z Joint learning and dictionary construction for pattern recognition Pham, DucSon Venkatesh, Svetha Not known We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small. 2008 Conference Paper http://hdl.handle.net/20.500.11937/7656 10.1109/CVPR.2008.4587408 IEEE restricted |
| spellingShingle | Pham, DucSon Venkatesh, Svetha Joint learning and dictionary construction for pattern recognition |
| title | Joint learning and dictionary construction for pattern recognition |
| title_full | Joint learning and dictionary construction for pattern recognition |
| title_fullStr | Joint learning and dictionary construction for pattern recognition |
| title_full_unstemmed | Joint learning and dictionary construction for pattern recognition |
| title_short | Joint learning and dictionary construction for pattern recognition |
| title_sort | joint learning and dictionary construction for pattern recognition |
| url | http://hdl.handle.net/20.500.11937/7656 |