NDLI: Orthogonal margin discriminant projection for dimensionality reduction

Content Provider	Springer Nature Link
Author	He, Jinrong Wu, Di Xiong, Naixue Wu, Chuansheng
Copyright Year	2015
Abstract	Dimensionality reduction aims to represent high-dimensional data with much smaller number of features, which plays as a preprocessing step to remove the insignificant and irrelevant features in many machine learning applications, resulting in lower computational cost and better performance of classifiers. In most cases, the data points can be well classified with margin samples which are defined as furthest intra-class samples and nearest inter-class samples. Motivated by this observation, this paper proposes a linear supervised dimensionality reduction method called orthogonal margin discriminant projection (OMDP). After OMDP projection, intra-class data points become more compact and inter-class data points become more separated. Extensive experiments have been conducted to evaluate the proposed OMDP algorithm using several benchmark face data sets. The experimental results confirm the effectiveness of the proposed method.
Starting Page	2095
Ending Page	2110
Page Count	16
File Format	PDF
ISSN	09208542
Journal	The Journal of Supercomputing
Volume Number	72
Issue Number	6
e-ISSN	15730484
Language	English
Publisher	Springer US
Publisher Date	2015-06-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dimensionality reduction Feature extraction Face recognition Margin sample Orthogonal margin discriminant projection Programming Languages, Compilers, Interpreters Processor Architectures Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Information Systems Hardware and Architecture Software

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Orthogonal margin discriminant projection for dimensionality reduction

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