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Belkin, M. and Niyogi, P. (2001) Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In Advances in Neural Information Processing Systems, MIT USA, 585-591.

has been cited by the following article:

• TITLE: Laplacian Maximum Margin Criterion for Image Recognition

  AUTHORS: Fang Chen, Jing Wang, Quanxue Gao

  KEYWORDS: Laplacian Embedding, Local Intrinsic Structure, Global Structure, Face Recognition

  JOURNAL NAME: Journal of Computer and Communications, Vol.3 No.11, November 19, 2015

  ABSTRACT: Previous works have demonstrated that Laplacian embedding can well preserve the local intrinsic structure. However, it ignores the diversity and may impair the local topology of data. In this paper, we build an objective function to learn the local intrinsic structure that characterizes both the local similarity and diversity of data, and then combine it with global structure to build a scatter difference criterion. Experimental results in face recognition show the effectiveness of our proposed approach.