Analysis on Supervised Neighborhood Preserving Embedding

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概要

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• Neighborhood Preserving Embedding (NPE) is an unsupervised dimensionality reduction technique. Hence, it is lacking of discriminative capability. Zeng and Luo have proposed Supervised Neighborhood Preserving Embedding (SNPE), which uses class information of training samples to better describe data intrinsic structure. The robustness of SNPE has been demonstrated since it yields promising recognition results. However, there is no theoretical analysis to explain the good performance. Here, we show analytically that the neighborhood discriminant criterion, which manifested in the objective function of SNPE, is close resembled to Fisher discriminant criterion. SNPE is evaluated in ORL and PIE face databases. The inclusion of class information in data learning results superior performance of SNPE to NPE.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• J. Andrew

  School of Electrical and Electronics Engineering, Yonsei University

• Pang Ying

  Faculty of Information Science and Technology, Multimedia University

関連論文

• Analysis on Supervised Neighborhood Preserving Embedding

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