Computer Science ›› 2017, Vol. 44 ›› Issue (7): 309-314.doi: 10.11896/j.issn.1002-137X.2017.07.056

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Hypergraph Dual Regularization Concept Factorization Algorithm and Its Application in Data Representation

YE Jun and JIN Zhong   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The concept factorization(CF) algorithm can not take the geometric structures of both the data manifold and the feature manifold into account simultaneously.And CF algorithm can not consider the high-order relationship among samples.In this paper,a novel algorithm called hypergraph dual regularization concept factorization(DHCF) algorithm was proposed,which encodes the high-order geometric structure information of data and feature spaces by constructing two undirected weighted hypergraph Laplacian regularize term,respectively.By this way,the proposed method can overcome the deficiency that traditional graph model expresses pair-wise relationship only.Moreover,we developed the iterative updating optimization schemes for DHCF,and provided the convergence proof of our optimization scheme.Experimental results on TDT2 document datasets,PIE and COIL20 image datasets demonstrate the effectiveness of our method.

Key words: CF,Hypergraph learning,Dual regularized,Manifold learning,Clustering

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