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Kernel-view based discriminant approach for embedded feature extraction in high-dimensional space
Miao Cheng1,2; Bin Fang1; Chi-Man Pun2; Yuan Yan Tang1,2
2011-04-01
Source PublicationNeurocomputing
ISSN0925-2312
Volume74Issue:9Pages:1478-1484
Abstract

Derived from the traditional manifold learning algorithms, local discriminant analysis methods identify the underlying submanifold structures while employing discriminative information for dimensionality reduction. Mathematically, they can all be unified into a graph embedding framework with different construction criteria. However, such learning algorithms are limited by the curse-of-dimensionality if the original data lie on the high-dimensional manifold. Different from the existing algorithms, we consider the discriminant embedding as a kernel analysis approach in the sample space, and a kernel-view based discriminant method is proposed for the embedded feature extraction, where both PCA pre-processing and the pruning of data can be avoided. Extensive experiments on the high-dimensional data sets show the robustness and outstanding performance of our proposed method. 

KeywordCurse-of-dimensionality Dimensionality Reduction Feature Extraction Kernel Analysis Local Discriminant Analysis
DOIhttps://doi.org/10.1016/j.neucom.2011.01.004
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000290078600020
PublisherELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
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被引频次[WOS]:4   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorMiao Cheng
Affiliation1.Department of Computer Science, Chongqing University, Chongqing, China
2.Department of Computer and Information Science, University of Macau, Macau
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
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GB/T 7714
Miao Cheng,Bin Fang,Chi-Man Pun,et al. Kernel-view based discriminant approach for embedded feature extraction in high-dimensional space[J]. Neurocomputing,2011,74(9):1478-1484.
APA Miao Cheng,Bin Fang,Chi-Man Pun,&Yuan Yan Tang.(2011).Kernel-view based discriminant approach for embedded feature extraction in high-dimensional space.Neurocomputing,74(9),1478-1484.
MLA Miao Cheng,et al."Kernel-view based discriminant approach for embedded feature extraction in high-dimensional space".Neurocomputing 74.9(2011):1478-1484.
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