Co-Regularization for Classification
文献类型：会议论文
| 作者 | Li, Yang; Tao, Dapeng; Liu, Weifeng; Wang, Yanjiang |
| 出版日期 | 2014 |
| 会议名称 | Security, Pattern Analysis, and Cybernetics (SPAC), 2014 International Conference on |
| 会议地点 | 中国 |
| 英文摘要 | Semi-supervised learning algorithms that combine labeled and unlabeled data receive significant interests in recent years and are successfully deployed in many practical data mining applications. Manifold regularization, one of the most representative works, tries to explore the geometry of the intrinsic data probability distribution by penalizing the classification function along the implicit manifold. Although existing manifold regularization, including Laplacian regularization (LR) and Hessian regularization (HR), yields significant benefits for partially labeled classification, it is observed that LR suffers from the poor generalization and HR exhibits the characteristic of instability, both manifold regularization could not accurately reflect the ground-truth. To remedy the problems in single manifold regularization and approximate the intrinsic manifold, we propose Manifold Regularized Co-Training (Co-Re) framework, which combines the manifold regularization (LR and HR) and the algorithm co-training. Extensive experiments on the USAA video dataset are conducted and validate the effectiveness of Co-Re by comparing it with baseline manifold regularization algorithms. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/5596]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2014 |
| 推荐引用方式 GB/T 7714 | Li, Yang,Tao, Dapeng,Liu, Weifeng,et al. Co-Regularization for Classification[C]. 见:Security, Pattern Analysis, and Cybernetics (SPAC), 2014 International Conference on. 中国. |
入库方式： OAI收割
来源：深圳先进技术研究院
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