Computer Science ›› 2014, Vol. 41 ›› Issue (4): 273-279.

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New Supervised Manifold Learning Method Based on MMC

YUAN Min,YANG Rui-guo,YUAN Yuan and LEI Ying-ke   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Based on the analysis of local spline embedding (LSE) method,we proposed an efficient feature extraction algorithm called orthogonal local spline discriminant projection (O-LSDP).By introducing an explicit linear mapping,constructing different translation and rescaling models for different classes as well as orthogonalizing feature subspace,O-LSDP can effectively circumvent the two major shortcomings of the original LSE algorithm,i.e.,out-of-sample and unsupervised learning.O-LSDP not only inherits the advantages of LSE which uses local tangent space as a representation of the local geometry so as to preserve the local structure,but also makes full use of class information and orthogonal subspace to significantly improve discriminant power.Extensive experiments on standard face databases and plant leaf data set verify the feasibility and effectiveness of the proposed algorithm.

Key words: Feature extraction,Subspace learning,Local spline embedding,Maximum margin criterion,Manifold learning

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