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Weinberger, K.Q., Packer, B.D. and Saul, L.K. (2005) Nonlinear Dimensionality Reduction by Semi-Definite Programming and Kernel Matrix Factorization. Proc. the Tenth Workshop Artificial Intelligence and Statistics (AISTATS- 2005), 381-388.

has been cited by the following article:

• 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.