A General Approach for Achieving Supervised Subspace Learning in Sparse Representation
Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature. Most of them are unsupervised algorithms that are applied to data without label scenarios. It is worth noting that the l...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8640013/ |
| _version_ | 1818737147551678464 |
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| author | Jianshun Sang Dezhong Peng Yongsheng Sang |
| author_facet | Jianshun Sang Dezhong Peng Yongsheng Sang |
| author_sort | Jianshun Sang |
| collection | DOAJ |
| description | Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature. Most of them are unsupervised algorithms that are applied to data without label scenarios. It is worth noting that the label information is available in some application scenarios such as face recognition where the above-mentioned dimensionality reduction techniques cannot employ the label information to improve their performance. Therefore, under these labeled scenarios, it is necessary to transform an unsupervised subspace learning algorithm into the corresponding supervised algorithm to improve the performance. In this paper, we propose an approach which can be used as a general way for developing a corresponding supervised algorithm based on any unsupervised subspace learning algorithm using sparse representation. Moreover, by utilizing the proposed approach, we achieve a new supervised subspace learning algorithm named supervised principal coefficients embedding (SPCE). We show that SPCE establishes the advantages over the state-of-the-art supervised subspace learning algorithm. |
| first_indexed | 2024-12-18T00:48:25Z |
| format | Article |
| id | doaj.art-e8020f65a1924f0281628d7f961f16e2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:48:25Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e8020f65a1924f0281628d7f961f16e22022-12-21T21:26:44ZengIEEEIEEE Access2169-35362019-01-017740177402810.1109/ACCESS.2019.28989238640013A General Approach for Achieving Supervised Subspace Learning in Sparse RepresentationJianshun Sang0Dezhong Peng1https://orcid.org/0000-0002-0987-8472Yongsheng Sang2https://orcid.org/0000-0001-5236-439XMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, ChinaMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, ChinaMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, ChinaOver the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature. Most of them are unsupervised algorithms that are applied to data without label scenarios. It is worth noting that the label information is available in some application scenarios such as face recognition where the above-mentioned dimensionality reduction techniques cannot employ the label information to improve their performance. Therefore, under these labeled scenarios, it is necessary to transform an unsupervised subspace learning algorithm into the corresponding supervised algorithm to improve the performance. In this paper, we propose an approach which can be used as a general way for developing a corresponding supervised algorithm based on any unsupervised subspace learning algorithm using sparse representation. Moreover, by utilizing the proposed approach, we achieve a new supervised subspace learning algorithm named supervised principal coefficients embedding (SPCE). We show that SPCE establishes the advantages over the state-of-the-art supervised subspace learning algorithm.https://ieeexplore.ieee.org/document/8640013/Subspace learningsparse representationsupervised algorithmmanifold learningdimensionality reduction |
| spellingShingle | Jianshun Sang Dezhong Peng Yongsheng Sang A General Approach for Achieving Supervised Subspace Learning in Sparse Representation IEEE Access Subspace learning sparse representation supervised algorithm manifold learning dimensionality reduction |
| title | A General Approach for Achieving Supervised Subspace Learning in Sparse Representation |
| title_full | A General Approach for Achieving Supervised Subspace Learning in Sparse Representation |
| title_fullStr | A General Approach for Achieving Supervised Subspace Learning in Sparse Representation |
| title_full_unstemmed | A General Approach for Achieving Supervised Subspace Learning in Sparse Representation |
| title_short | A General Approach for Achieving Supervised Subspace Learning in Sparse Representation |
| title_sort | general approach for achieving supervised subspace learning in sparse representation |
| topic | Subspace learning sparse representation supervised algorithm manifold learning dimensionality reduction |
| url | https://ieeexplore.ieee.org/document/8640013/ |
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