文章基本信息

标题：Discriminant Neighborhood Structure Embedding Using Trace Ratio Criterion for Image Recognition
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作者：Jing Wang ; Fang Chen ; Quanxue Gao 等
期刊名称：Journal of Computer and Communications
印刷版ISSN：2327-5219
电子版ISSN：2327-5227
出版年度：2015
卷号：03
期号：11
页码：64-70
DOI：10.4236/jcc.2015.311011
语种：English
出版社：Scientific Research Publishing
摘要：Dimensionality reduction is very important in pattern recognition, machine learning, and image recognition. In this paper, we propose a novel linear dimensionality reduction technique using trace ratio criterion, namely Discriminant Neighbourhood Structure Embedding Using Trace Ratio Criterion (TR-DNSE). TR-DNSE preserves the local intrinsic geometric structure, characterizing properties of similarity and diversity within each class, and enforces the separability between different classes by maximizing the sum of the weighted distances between nearby points from different classes. Experiments on four image databases show the effectiveness of the proposed approach.
关键词：Dimensionality Reduction;Manifold Learning;Variability;Trace Ratio