Maximum Neighborhood Margin Discriminant Projection for Classification
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...
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Series: | The Scientific World Journal |
Online Access: | http://dx.doi.org/10.1155/2014/186749 |
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doaj-abaa7c89006c41d781a1807ab54c69502020-11-25T02:15:33ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/186749186749Maximum Neighborhood Margin Discriminant Projection for ClassificationJianping Gou0Yongzhao Zhan1Min Wan2Xiangjun Shen3Jinfu Chen4Lan Du5School of Computer Science and Telecommunication Engineering, JiangSu University, ZhenJiang, JiangSu 212013, ChinaSchool of Computer Science and Telecommunication Engineering, JiangSu University, ZhenJiang, JiangSu 212013, ChinaSchool of Mathematics and Computer Engineering, Xihua University, Chengdu, Sichuan 610039, ChinaSchool of Computer Science and Telecommunication Engineering, JiangSu University, ZhenJiang, JiangSu 212013, ChinaSchool of Computer Science and Telecommunication Engineering, JiangSu University, ZhenJiang, JiangSu 212013, ChinaDepartment of Computing, Macquarie University, Sydney, NSW 2109, AustraliaWe 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.http://dx.doi.org/10.1155/2014/186749 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jianping Gou Yongzhao Zhan Min Wan Xiangjun Shen Jinfu Chen Lan Du |
spellingShingle |
Jianping Gou Yongzhao Zhan Min Wan Xiangjun Shen Jinfu Chen Lan Du Maximum Neighborhood Margin Discriminant Projection for Classification The Scientific World Journal |
author_facet |
Jianping Gou Yongzhao Zhan Min Wan Xiangjun Shen Jinfu Chen Lan Du |
author_sort |
Jianping Gou |
title |
Maximum Neighborhood Margin Discriminant Projection for Classification |
title_short |
Maximum Neighborhood Margin Discriminant Projection for Classification |
title_full |
Maximum Neighborhood Margin Discriminant Projection for Classification |
title_fullStr |
Maximum Neighborhood Margin Discriminant Projection for Classification |
title_full_unstemmed |
Maximum Neighborhood Margin Discriminant Projection for Classification |
title_sort |
maximum neighborhood margin discriminant projection for classification |
publisher |
Hindawi Limited |
series |
The Scientific World Journal |
issn |
2356-6140 1537-744X |
publishDate |
2014-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2014/186749 |
work_keys_str_mv |
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_version_ |
1724895484850470912 |