信息与控制2017，Vol.46Issue(1)：53-59,64,8.DOI:10.13976/j.cnki.xk.2017.0053
几何结构保持非负矩阵分解的数据表达方法
A Geometric Structure Preserving Non-negative Matrix Factorization for Data Representation
摘要
Abstract
As a linear dimensionality reduction technique,non-negative matrix factorization (NMF) has been widely used in many fields.However,NMF can only perform semantic factorization in Euclidean space,and it fails to discover the intrinsic geometrical structure of high-dimensional data distribution.To address this issue,in this paper,we propose a new non-negative matrix faetorization algorithm,known as the structure preserving nonnegative matrix factorization (SPNMF).Compared with the existing NMF,our SPNMF method effectively exploits the local affinity structure and distant repulsion structure among data samples.Specifically,we incorporate the local and distant structure preservation terms into the NMF framework and then give an alternative optimization method for SPNMF.Due to prior knowledge from the structure preservation term,SPNMF can learn a good low-dimensional representation.Experimental results on some facial image dataset clustering show the significantly improved performance of SPNMF compared with other state-of-the-art algorithms.关键词
非负矩阵分解/结构保持/图正则化/补空间/图像聚类Key words
non-negative matrix factorization/structure preservation/graph regularization/complementary space/image clustering分类
信息技术与安全科学引用本文复制引用
李冰锋,唐延东,韩志..几何结构保持非负矩阵分解的数据表达方法[J].信息与控制,2017,46(1):53-59,64,8.基金项目
国家自然科学基金资助项目(61303168) （61303168）
