Computer Science ›› 2016, Vol. 43 ›› Issue (6): 312-315.doi: 10.11896/j.issn.1002-137X.2016.06.062

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Kernel-based Supervised Neighborhood Projection Analysis Algorithm

ZHENG Jian-wei, KONG Chen-chen, WANG Wan-liang, QIU Hong and ZHANG Hang-ke   

  • Online:2018-12-01 Published:2018-12-01

Abstract: A new algorithm called KSNPA which exhibits a nonlinear form of discriminative elastic embedding (DEE) was proposed.KSNPA integrates class labels and linear projection matrix into the final objective function,as well as uses kernel function to deal with nonlinear embedding situation.According to two different strategies for optimizing the objective function,the algorithm is divided into kernel-based supervised neighborhood projection analysis algorithm 1(KSNPA1) and supervised neighborhood projection analysis algorithm 2 (KSNPA2).Furthermore,a deliberately selected search direction,termed as Laplacian Direction,is applied in KSNPA1 for achieving faster convergence rate and lower computational complexit.Experimental results on several databases demonstrate that the proposed algorithm achieves powerful pattern revealing capability for complex manifold data.Moreover,the algorithm is more efficient and robust than DEE and related dimensionality reduction algorithms.

Key words: Elastic embedding,Kernel method,Projection analysis,Supervised learning

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