摘要
非负矩阵在表征多视图数据时没有考虑数据本身的流型结构,不能有效表达数据内部信息。为此,提出一种基于Hessian正则化的非负矩阵分解算法。利用Hessian泛函的L2模,保持样本局部拓扑结构,并扩展成基于Hessian正则化的联合非负矩阵分解算法,以对多视图数据进行变换。实验结果表明,基于Hessian正则化的非负矩阵分解算法和基于Hessian正则化的联合非负矩阵分解算法的聚类精度以及互信息值都有较大提高,2种算法的数据变化性能都优于传统非负矩阵分解算法。
Non-negative matrix does not consider the manifold of data when represents multi-view data,which results in the ineffective express of the data internal expression. In this paper,Hessian regularized Non-negative Matrix Factorization（ NMF） is proposed. By using the L2 model of Hessian functional,the local topology of the sample is preserved and the algorithm is further extended into Hessian Regularized Joint Non-negative Matrix Factorization（ HR-J-NMF） to work on multi-view data. Experimental results show that the Hessian regularized NMF and the HR-J-NMF have a great improvement in both clustering accuracy and mutual information value. The performance of the two algorithms is superior to that of the traditional NMF algorithm.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第11期134-139,共6页
Computer Engineering
基金
国家自然科学基金面上项目(61471231
81627804)
关键词
Hessian正则化
回归模型
非负矩阵分解
多视图数据
聚类
Hessian regularization
regression model
Non-negative Matrix Factorization （NMF）
multi-view data
clustering
