Discriminant Neighborhood Structure Embedding Using Trace Ratio Criterion for Image Recognition

• 作者: Fang Chen Jing Wang Quanxue Gao
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: Dimensionality Reduction MANIFOLD Learning Variability TRACE RATIO
• 年，卷（期）: 2015,（11）
• 页码范围: 64-70
• 页数: 7页
• 分类号: TP39

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.