Discriminant Neighborhood Structure Embedding Using Trace Ratio Criterion for Image Recognition

Jing Wang¹, Fang Chen¹, Quanxue Gao^1,2

¹School of Telecommunications Engineering, Xidian University, Xi’an, China.
²State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China.

DOI: 10.4236/jcc.2015.311011                

Abstract

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.

Keywords

Dimensionality Reduction, Manifold Learning, Variability, Trace Ratio

Conflicts of Interest

The authors declare no conflicts of interest.

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