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Maximum Neighborhood Margin Discriminant Projection for Classification
Joint Authors
Chen, Jinfu
Wan, Min
Gou, Jianping
Shen, Xiangjun
Du, Lan
Yong-zhao, Zhan
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-20
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data.
It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters.
By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes.
To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA.
The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.
American Psychological Association (APA)
Gou, Jianping& Yong-zhao, Zhan& Wan, Min& Shen, Xiangjun& Chen, Jinfu& Du, Lan. 2014. Maximum Neighborhood Margin Discriminant Projection for Classification. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-1048654
Modern Language Association (MLA)
Gou, Jianping…[et al.]. Maximum Neighborhood Margin Discriminant Projection for Classification. The Scientific World Journal No. 2014 (2014), pp.1-16.
https://search.emarefa.net/detail/BIM-1048654
American Medical Association (AMA)
Gou, Jianping& Yong-zhao, Zhan& Wan, Min& Shen, Xiangjun& Chen, Jinfu& Du, Lan. Maximum Neighborhood Margin Discriminant Projection for Classification. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-1048654
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1048654