A supervised non-linear dimensionality reduction approach for manifold learning

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In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other.Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance).

论文关键词:Supervised manifold learning,Non-linear dimensionality reduction,Discriminant analysis,Face recognition

论文评审过程:Received 18 April 2011, Revised 29 November 2011, Accepted 10 December 2011, Available online 19 December 2011.

论文官网地址:https://doi.org/10.1016/j.patcog.2011.12.006