Recursive support vector machines for dimensionality reduction
文献类型：期刊论文
| 作者 | Tao, Qing1,2; Chu, Dejun2; Wang, Jue1 |
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS
 |
| 出版日期 | 2008 |
| 卷号 | 19期号:1页码:189-193 |
| 关键词 | classification dimensionality reduction feature extraction projection recursive support vector machines (RSVMs) support vector machines (SVMs). |
| 英文摘要 | The usual dimensionality reduction technique in supervised learning is mainly based on linear discriminant analysis (LDA), but it suffers from singularity or undersampled problems. On the other hand, a regular support vector machine (SVM) separates the data only in terms of one single direction of maximum margin, and the classification accuracy may be not good enough. In this letter, a recursive SVM (RSVM) is presented, in which several orthogonal directions that best separate the data with the maximum margin are obtained. Theoretical analysis shows that a completely orthogonal basis can be derived in feature subspace spanned by the training samples and the margin is decreasing along the recursive components in linearly separable cases. As a result, a new dimensionality reduction technique based on multilevel maximum margin components and then a classifier with high accuracy are achieved. Experiments in synthetic and several real data sets show that RSVM using multilevel maximum margin features can do efficient dimensionality reduction and outperform regular SVM in binary classification problems. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | FISHER LINEAR DISCRIMINANT ; FACE RECOGNITION ; THEORETICAL-ANALYSIS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000252516700017 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9639]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100080, Peoples R China 2.New Star Res Inst Appl Technol, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tao, Qing,Chu, Dejun,Wang, Jue. Recursive support vector machines for dimensionality reduction[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2008,19(1):189-193. |
| APA | Tao, Qing,Chu, Dejun,&Wang, Jue.(2008).Recursive support vector machines for dimensionality reduction.IEEE TRANSACTIONS ON NEURAL NETWORKS,19(1),189-193. |
| MLA | Tao, Qing,et al."Recursive support vector machines for dimensionality reduction".IEEE TRANSACTIONS ON NEURAL NETWORKS 19.1(2008):189-193. |
入库方式： OAI收割
来源：自动化研究所
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