Learning linear PCA with convex semi-definite programming
文献类型：期刊论文
| 作者 | Tao, Qing; Wu, Gao-wei ; Wang, Jue
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2007-10-01 |
| 卷号 | 40期号:10页码:2633-2640 |
| 关键词 | principal component analysis statistical learning theory support vector machines margin maximal margin algorithm semi-definite programming robustness |
| 英文摘要 | The aim of this paper is to learn a linear principal component using the nature of support vector machines (SVMs). To this end, a complete SVM-like framework of linear PCA (SVPCA) for deciding the projection direction is constructed, where new expected risk and margin are introduced. Within this framework, a new semi-definite programming problem for maximizing the margin is formulated and a new definition of support vectors is established. As a weighted case of regular PCA, our SVPCA coincides with the regular PCA if all the samples play the same part in data compression. Theoretical explanation indicates that SVPCA is based on a margin-based generalization bound and thus good prediction ability is ensured. Furthermore, the robust form of SVPCA with a interpretable parameter is achieved using the soft idea in SVMs. The great advantage lies in the fact that SVPCA is a learning algorithm without local minima because of the convexity of the semi-definite optimization problems. To validate the performance of SVPCA, several experiments are conducted and numerical results have demonstrated that their generalization ability is better than that of regular PCA. Finally, some existing problems are also discussed. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | PRINCIPAL COMPONENT ANALYSIS ; MATRIX |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000247650000003 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9410]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Lab Complex Syst & Intelligence Sci, Beijing 100080, Peoples R China 2.New Star Res Inst Appl Tech, Hefei 230031, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Div Intelligent Software Syst, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tao, Qing,Wu, Gao-wei,Wang, Jue. Learning linear PCA with convex semi-definite programming[J]. PATTERN RECOGNITION,2007,40(10):2633-2640. |
| APA | Tao, Qing,Wu, Gao-wei,&Wang, Jue.(2007).Learning linear PCA with convex semi-definite programming.PATTERN RECOGNITION,40(10),2633-2640. |
| MLA | Tao, Qing,et al."Learning linear PCA with convex semi-definite programming".PATTERN RECOGNITION 40.10(2007):2633-2640. |
入库方式： OAI收割
来源：自动化研究所
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