Discriminant neighborhood embedding for classification

作者:

Highlights:

摘要

In this paper a novel subspace learning method called discriminant neighborhood embedding (DNE) is proposed for pattern classification. We suppose that multi-class data points in high-dimensional space tend to move due to local intra-class attraction or inter-class repulsion and the optimal embedding from the point of view of classification is discovered consequently. After being embedded into a low-dimensional subspace, data points in the same class form compact submanifod whereas the gaps between submanifolds corresponding to different classes become wider than before. Experiments on the UMIST and MNIST databases demonstrate the effectiveness of our method.

论文关键词:Discriminant neighborhood embedding (DNE),Intra-class attraction,Inter-class repulsion,Pattern classification

论文评审过程:Received 17 November 2005, Accepted 8 May 2006, Available online 10 July 2006.

论文官网地址:https://doi.org/10.1016/j.patcog.2006.05.011