基于对偶图正则化的多层概念分解算法.

In order to further excavate the hidden information between data, under the framework of multilayer concept factorization algorithm, this paper proposed a novel algorithm called dual-graph regularized multilayer concept factorization algorithm, which encoded the geometric structure information of data and feature spaces by constructing two Laplacian regularize term in each layer factorization, respectively. By this way, the proposed method could learn features in a hierarchical manner, and thus provided a better chance for learning meaningful features from the complex data. Moreover, it developed the iterative updating optimization scheme for DHCF, and also provided the convergence proof of the optimization scheme. Experimental results on TDT2 document datasets, PIE and COIL20 image datasets demonstrate the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]