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Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction
Li BF(李冰锋); Tang YD(唐延东); Han Z(韩志)
作者部门机器人学研究室
发表期刊MATHEMATICAL PROBLEMS IN ENGINEERING
ISSN1024-123X
2016
卷号2016页码:1-14
收录类别SCI ; EI
EI收录号20163202703159
WOS记录号WOS:000379478900001
产权排序1
资助机构Natural Science Foundation of China [61333019, 61303168]
摘要As a linear dimensionality reduction method, nonnegative matrix factorization (NMF) has been widely used in many fields, such as machine learning and data mining. However, there are still two major drawbacks for NMF: (a) NMF can only perform semantic factorization in Euclidean space, and it fails to discover the intrinsic geometrical structure of high-dimensional data distribution. (b) NMFsuffers from noisy data, which are commonly encountered in real-world applications. To address these issues, in this paper, we present a new robust structure preserving nonnegative matrix factorization (RSPNMF) framework. In RSPNMF, a local affinity graph and a distant repulsion graph are constructed to encode the geometrical information, and noisy data influence is alleviated by characterizing the data reconstruction term of NMF with l(2),(1)-norm instead of l(2)-norm. With incorporation of the local and distant structure preservation regularization term into the robust NMF framework, our algorithm can discover a low-dimensional embedding subspace with the nature of structure preservation. RSPNMF is formulated as an optimization problem and solved by an effective iterativemultiplicative update algorithm. Experimental results on some facial image datasets clustering show significant performance improvement of RSPNMF in comparison with the state-of-the-art algorithms.
语种英语
WOS标题词Science & Technology ; Technology ; Physical Sciences
WOS类目Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
关键词[WOS]FACE RECOGNITION ; REPRESENTATION ; MANIFOLD ; OBJECTS ; PARTS
WOS研究方向Engineering ; Mathematics
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文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/18828
专题机器人学研究室
通讯作者Li BF(李冰锋)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式
GB/T 7714
Li BF,Tang YD,Han Z. Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction[J]. MATHEMATICAL PROBLEMS IN ENGINEERING,2016,2016:1-14.
APA Li BF,Tang YD,&Han Z.(2016).Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction.MATHEMATICAL PROBLEMS IN ENGINEERING,2016,1-14.
MLA Li BF,et al."Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction".MATHEMATICAL PROBLEMS IN ENGINEERING 2016(2016):1-14.
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