Abstract
Semi-supervised learning methods attempt to improve the performance of a supervised or an unsupervised learning in the presence of side information. This side information can be in the form of unlabeled samples in the supervised case or pairwise constraints in the unsupervised case
Keywords
- Side Information
- Locally Linear Embedding
- Locality Preserve Projection
- Pairwise Constraint
- Unlabeled Sample
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Li, JB., Chu, SC., Pan, JS. (2014). Kernel Semi-Supervised Learning-Based Face Recognition. In: Kernel Learning Algorithms for Face Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0161-2_7
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DOI: https://doi.org/10.1007/978-1-4614-0161-2_7
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