基于对偶图正则化的多层概念分解算法

Dual-graph regularized multilayer concept factorization

为了进一步挖掘数据间的隐藏信息,在多层概念分解算法的框架下,考虑每一层分解下的数据流形和特征流形,提出了一种基于对偶图正则化的多层概念分解算法。该算法通过对数据的逐层分解,以分层的方式学习,并在每一层分解数据中构建数据空间和特征属性空间的拉普拉斯图,用于反映数据流形和特征流形的多元几何结构信息,从而能够更好地从复杂数据中提取出更有效的特征。采用交替迭代的方法求解算法的目标函数并证明了算法的收敛性。通过在三个真实数据库(TDT2、PIE、COIL20)上的实验表明,该方法在数据的聚类表示效果方面优于其他方法。

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.