Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
文献类型：期刊论文
| 作者 | Wei Zhang; Zhouchen Lin; Xiaoou Tang |
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2011 |
| 卷号 | 23期号:4页码:600-611 |
| 英文摘要 | Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm forsupervised feature extraction. Recently, unlabeled data have been utilized to improve LDA. However, the intrinsic problems of LDA still exist and only the similarity among the unlabeled data is utilized. In this paper, we propose a novel algorithm, called Semisupervised Semi-Riemannian Metric Map (S(3)RMM), following the geometric framework of semi-Riemannian manifolds. S(3)RMM maximizes the discrepancy of the separability and similarity measures of scatters formulated by using semi-Riemannian metric tensors. The metric tensor of each sample is learned via semisupervised regression. Our method can also be a general framework for proposing new semisupervised algorithms, utilizing the existing discrepancy-criterion-based algorithms. The experiments demonstrated on faces and handwritten digits show that S(3)RMM is promising for semisupervised feature extraction. |
| 收录类别 | SCI |
| 原文出处 | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5560647&tag=1 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/3175]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
| 推荐引用方式 GB/T 7714 | Wei Zhang,Zhouchen Lin,Xiaoou Tang. Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2011,23(4):600-611. |
| APA | Wei Zhang,Zhouchen Lin,&Xiaoou Tang.(2011).Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,23(4),600-611. |
| MLA | Wei Zhang,et al."Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 23.4(2011):600-611. |
入库方式： OAI收割
来源：深圳先进技术研究院
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