Journal
IET IMAGE PROCESSING
Volume 7, Issue 2, Pages 131-136Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-ipr.2012.0322
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Funding
- National Basic Research Program of China (973 Program) [2013CB329402, NCET-10-0668]
- National Science Foundation of China [61072108, 60971112, 61272282]
- Fundamental Research Funds for the Central Universities [K50511020011]
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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.
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