基于干净数据的流形正则化非负矩阵分解

Manifold regularized nonnegative matrix factorization based on clean data

现有的非负矩阵分解(NMF)算法往往基于欧氏距离来设计目标函数,对噪声比较敏感。为了增强算法的鲁棒性,提出一种基于干净数据的流形正则化非负矩阵分解(MRNMF/CD)算法。在MRNMF/CD算法中,把低秩约束、流形正则化和NMF技术无缝地融为一体,使算法性能较为优异。首先,通过添加低秩约束,MRNMF/CD可以从噪声数据中恢复干净数据,并获得数据的全局结构;其次,为了利用数据的局部几何结构信息,MRNMF/CD把流形正则化融入目标函数中。此外,还提出了一种求解MRNMF/CD的迭代算法,并从理论上分析了该求解算法的收敛性。在ORL、Yale和COIL20数据集上的实验结果表明,MRNMF/CD算法比现有的k-means、主成分分析(PCA)、NMF和图正则化非负矩阵分解(GNMF)算法具有更好的识别准确性。

The existing Nonnegative Matrix Factorization(NMF)algorithms are often designed based on Euclidean distance,which makes the algorithms sensitive to noise.In order to enhance the robustness of these algorithms,a Manifold Regularized Nonnegative Matrix Factorization based on Clean Data(MRNMF/CD)algorithm was proposed.In MRNMF/CD algorithm,the low-rank constraints,manifold regularization and NMF technologies were seamlessly integrated,which makes the algorithm perform relatively excellent.Firstly,by adding the low-rank constraints,MRNMF/CD can recover clean data from noisy data and obtain the global structure of the data.Secondly,in order to use the local geometric structure information of the data,manifold regularization was incorporated into the objective function by MRNMF/CD.In addition,an iterative algorithm for solving MRNMF/CD was proposed,and the convergence of this solution algorithm was analyzed theoretically.Experimental results on ORL,Yale and COIL20 datasets show that MRNMF/CD algorithm has better accuracy than the existing algorithms including k-means,Principal Component Analysis(PCA),NMF and Graph Regularized Nonnegative Matrix Factorization(GNMF).