Maximum neighborhood margin discriminant projection for classification

Jianping Gou*, Yongzhao Zhan, Min Wan, Xiangjun Shen, Jinfu Chen, Lan Du

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
8 Downloads (Pure)


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.

Original languageEnglish
Article number186749
Pages (from-to)1-16
Number of pages16
JournalThe Scientific World Journal
Publication statusPublished - 2014

Bibliographical note

Copyright the Author(s) 2014. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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