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| Content Provider | IET Digital Library |
|---|---|
| Author | Yang, Shuyuan Wang, Xiuxiu Wang, Min Han, Yue Jiao, Licheng |
| Abstract | In this study, the authors propose a new semi-supervised low-rank representation graph for pattern recognition. A collection of samples is jointly coded by the recently developed low-rank representation (LRR), which better captures the global structure of data and implements more robust subspace segmentation from corrupted samples. By using the calculated LRR coefficients of both labelled and unlabelled samples as the graph weights, a low-rank representation graph is established in a parameter-free manner under the framework of semi-supervised learning. Some experiments are taken on the benchmark database to investigate the performance of the proposed method and the results show that it is superior to other related semi-supervised graphs. |
| Starting Page | 131 |
| Ending Page | 136 |
| Page Count | 6 |
| ISSN | 17519659 |
| Volume Number | 7 |
| e-ISSN | 17519667 |
| Issue Number | Issue 2, Mar (2013) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/7/2 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2012.0322 |
| Journal | IET Image Processing |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Combinatorial Mathematics Computer Vision And Image Processing Technique Corrupted Samples Face Recognition Global Structure Graph Theory Graph Weight Image Recognition Image Representation Image Segmentation Learning in AI LRR Pattern Recognition Semisupervised Learning Semisupervised Low-rank Representation Graph SubSpace Segmentation Visual Database |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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