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An efficient regularized neighborhood discriminant analysis through QR decomposition
MIAO CHENG; BIN FANG; YUAN-YAN TANG; JING WEN
2008-12-01
Conference Name2008 International Conference on Wavelet Analysis and Pattern Recognition
Source PublicationProceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR
Volume1
Pages304-309
Conference Date30-31 Aug. 2008
Conference PlaceHong Kong
CountryChina
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Inspired by the concept of manifold learning, the discriminant embedding technologies aim to exploit lowdimensional discriminant manifold structure in the highdimensional space for dimension reduction and classification. However, such graph embedding framework based techniques usually suffer from the large complexity and small sample size (SSS) problem. To address the problem, we reformulate the laplacian matrix and propose a regularized neighborhood discriminant analysis method, namely RNDA, to discover the local discriminant information, which follows similar approach to regularized LDA. Compared with other discriminant embedding techniques, RNDA achieves efficiency by employing the QR decomposition as a pre-step. Experiments on face databases are presented to show the outstanding performance of the proposed method. 

KeywordDimension Reduction Discriminant Embedding Qr Decomposition Regularized Lda
DOIhttps://doi.org/10.1109/ICWAPR.2008.4635794
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000260168200055
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of computer science, The Chongqing University, Chongqing, P. R. China
Recommended Citation
GB/T 7714
MIAO CHENG,BIN FANG,YUAN-YAN TANG,et al. An efficient regularized neighborhood discriminant analysis through QR decomposition[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2008:304-309.
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