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Distance metric learning with penalized linear discriminant analysis
Chen Y(陈洋); Zhao XG(赵新刚); Han JD(韩建达)
Department机器人学研究室
Conference Name2010 1st IEEE International Conference on Progress in Informatics and Computing, PIC 2010
Conference DateDecember 10-12, 2010
Conference PlaceShanghai, China
Author of SourceIEEE Beijing Section; Shanghai Jiao Tong University; University of Texas at Dallas (UTD); Osaka University
Source PublicationProceedings of the 2010 IEEE International Conference on Progress in Informatics and Computing, PIC 2010
PublisherIEEE Computer Society
Publication PlacePiscataway, NJ
2010
Pages170-174
Indexed ByEI
EI Accession number20110713666465
Contribution Rank1
ISBN978-1-4244-6786-0
KeywordFisher Information Matrix Information Science Learning Algorithms Transfer Matrix Method
AbstractLinear 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.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/8672
Collection机器人学研究室
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang, China
2.School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, China
3.Graduate School, CAS, Beijing, China
Recommended Citation
GB/T 7714
Chen Y,Zhao XG,Han JD. Distance metric learning with penalized linear discriminant analysis[C]//IEEE Beijing Section; Shanghai Jiao Tong University; University of Texas at Dallas (UTD); Osaka University. Piscataway, NJ:IEEE Computer Society,2010:170-174.
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