Distance metric learning with penalized linear discriminant analysis
文献类型：会议论文
| 作者 | Chen Y(陈洋); Zhao XG(赵新刚) ; Han JD(韩建达)
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| 出版日期 | 2010 |
| 会议名称 | 2010 1st IEEE International Conference on Progress in Informatics and Computing, PIC 2010 |
| 会议日期 | December 10-12, 2010 |
| 会议地点 | Shanghai, China |
| 关键词 | Fisher information matrix Information science Learning algorithms Transfer matrix method |
| 页码 | 170-174 |
| 中文摘要 | Linear discriminant analysis has gained extensive applications in supervised classification and dimension reduction. In LDA formulation, original patterns with high dimension can be projected to lower dimension through a transfer matrix which is fundamental to clustering, nearest neighbor searches, and others. The transfer matrix is usually viewed as a distance metric. However, the classification accuracy under the LDA metric is neither optimal nor suboptimal because physical datasets often appear multimodal distribution. This paper proposes a penalized scheme for LDA to improve the classification rate by using the information of misclassified samples. This method is evaluated to be robust and effective by a great number of datasets from the machine learning repository. ©2010 IEEE. |
| 收录类别 | EI |
| 产权排序 | 1 |
| 会议主办者 | IEEE Beijing Section; Shanghai Jiao Tong University; University of Texas at Dallas (UTD); Osaka University |
| 会议录 | Proceedings of the 2010 IEEE International Conference on Progress in Informatics and Computing, PIC 2010
 |
| 会议录出版者 | IEEE Computer Society |
| 会议录出版地 | Piscataway, NJ |
| 语种 | 英语 |
| ISBN号 | 978-1-4244-6786-0 |
| 源URL | [http://ir.sia.cn/handle/173321/8672]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 推荐引用方式 GB/T 7714 | Chen Y,Zhao XG,Han JD. Distance metric learning with penalized linear discriminant analysis[C]. 见:2010 1st IEEE International Conference on Progress in Informatics and Computing, PIC 2010. Shanghai, China. December 10-12, 2010. |
入库方式： OAI收割
来源：沈阳自动化研究所
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