TITLE:
Discriminant Neighborhood Structure Embedding Using Trace Ratio Criterion for Image Recognition
AUTHORS:
Jing Wang, Fang Chen, Quanxue Gao
KEYWORDS:
Dimensionality Reduction, Manifold Learning, Variability, Trace Ratio
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.11,
November
19,
2015
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.