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题名: Distance metric learning with penalized linear discriminant analysis
作者: Chen Y(陈洋) ; Zhao XG(赵新刚) ; Han JD(韩建达)
作者部门: 机器人学研究室
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
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
出版日期: 2010
页码: 170-174
收录类别: EI
ISBN号: 9781424467860
关键词: Fisher information matrix ; Information science ; Learning algorithms ; Transfer matrix method
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.
语种: 英语
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8672
Appears in Collections:机器人学研究室_会议论文

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Recommended Citation:
陈洋; 赵新刚; 韩建达.Distance metric learning with penalized linear discriminant analysis.见:IEEE Computer Society.Proceedings of the 2010 IEEE International Conference on Progress in Informatics and Computing, PIC 2010,Piscataway, NJ ,2010,170-174
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