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Authors: Chunyu Wan ; Xuelian Yu ; Yun Zhou and Xuegang Wang

Affiliation: University of Electronic Science and Technology of China, China

ISBN: 978-989-758-018-5

Keyword(s): Radar Target Recognition, Feature Extraction, Nearest Feature Line, Uncorrelated Constraint, Kernel Technique.

Related Ontology Subjects/Areas/Topics: Classification ; Feature Selection and Extraction ; Kernel Methods ; Pattern Recognition ; Theory and Methods

Abstract: In this paper, a new subspace learning algorithm, called enhanced kernel uncorrelated discriminant nearest feature line analysis (EKUDNFLA), is presented. The aim of EKUDNFLA is to seek a feature subspace in which the within-class feature line (FL) distances are minimized and the between-class FL distances are maximized simultaneously. At the same time, an uncorrelated constraint is imposed to get statistically uncorrelated features, which contain minimum redundancy and ensure independence, and thus it is highly desirable in many practical applications. Optimizing an objective function in a kernel feature space, nonlinear features are extracted. In addition, a weighting coefficient is introduced to adjust the proportion between within-class and between-class information to get an optimal effect. Experimental results on radar target recognition with measured data demonstrate the effectiveness of the proposed method.

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Paper citation in several formats:
Wan C., Yu X., Zhou Y. and Wang X. (2014). Enhanced Kernel Uncorrelated Discriminant Nearest Feature Line Analysis for Radar Target Recognition.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 155-160. DOI: 10.5220/0004759701550160

@conference{icpram14,
author={Chunyu Wan and Xuelian Yu and Yun Zhou and Xuegang Wang},
title={Enhanced Kernel Uncorrelated Discriminant Nearest Feature Line Analysis for Radar Target Recognition},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={155-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004759701550160},
isbn={978-989-758-018-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Enhanced Kernel Uncorrelated Discriminant Nearest Feature Line Analysis for Radar Target Recognition
SN - 978-989-758-018-5
AU - Wan C.
AU - Yu X.
AU - Zhou Y.
AU - Wang X.
PY - 2014
SP - 155
EP - 160
DO - 10.5220/0004759701550160

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