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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)
Department机器人学研究室
Source PublicationMATHEMATICAL PROBLEMS IN ENGINEERING
ISSN1024-123X
2016
Volume2016Pages:1-14
Indexed BySCI ; EI
EI Accession number20163202703159
WOS IDWOS:000379478900001
Contribution Rank1
Funding OrganizationNatural Science Foundation of China [61333019, 61303168]
AbstractAs 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.
Language英语
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
WOS SubjectEngineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
WOS KeywordFACE RECOGNITION ; REPRESENTATION ; MANIFOLD ; OBJECTS ; PARTS
WOS Research AreaEngineering ; Mathematics
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/18828
Collection机器人学研究室
Corresponding AuthorLi BF(李冰锋)
Affiliation1.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
Recommended Citation
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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