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Alternative TitleA Geometric Structure Preserving Non-negative Matrix Factorization for Data Representation
李冰锋; 唐延东; 韩志
Source Publication信息与控制
Volume46Issue:1Pages:53-59, 64
Indexed ByCSCD
Contribution Rank1
Funding Organization国家自然科学基金资助项目(61303168)
Keyword非负矩阵分解 结构保持 图正则化 补空间 图像聚类
Other AbstractAs 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 factorization algorithm, known as the structure preserving non-negative 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.
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Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Corresponding Author李冰锋
Recommended Citation
GB/T 7714
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.
APA 李冰锋,唐延东,&韩志.(2017).几何结构保持非负矩阵分解的数据表达方法.信息与控制,46(1),53-59, 64.
MLA 李冰锋,et al."几何结构保持非负矩阵分解的数据表达方法".信息与控制 46.1(2017):53-59, 64.
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