Analysis on Supervised Neighborhood Preserving Embedding

IEICE Electronics Express

Ying Han Pang²  Andrew Teoh B. J.¹ 

[1]School of Electrical and Electronics Engineering, Yonsei University

[2]Faculty of Information Science and Technology, Multimedia University

关键词: Supervised Neighborhood Preserving Embedding;    class label information;    discriminant criterion;    face recognition;   

DOI  :  10.1587/elex.6.1631

学科分类：电子、光学、磁材料

来源: Denshi Jouhou Tsuushin Gakkai

【 摘 要 】

References(5)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.

【 授权许可】

Unknown