Discriminant neighborhood embedding for classification

Authors:
Wei Zhang;Xiangyang Xue;Hong Lu;Yue-Fei Guo
Affiliations:
Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and Engineering, Fudan University, Shanghai 200433, PR China;Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and Engineering, Fudan University, Shanghai 200433, PR China;Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and Engineering, Fudan University, Shanghai 200433, PR China;Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and Engineering, Fudan University, Shanghai 200433, PR China
Venue:
Pattern Recognition
Year:
2006

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Abstract

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.