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Discriminant Neighborhood Structure Embedding Using Trace Ratio Criterion for Image Recognition

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DOI: 10.4236/jcc.2015.311011    2,388 Downloads   2,619 Views  

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Wang, J. , Chen, F. and Gao, Q. (2015) Discriminant Neighborhood Structure Embedding Using Trace Ratio Criterion for Image Recognition. Journal of Computer and Communications, 3, 64-70. doi: 10.4236/jcc.2015.311011.

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