Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction
文献类型：期刊论文
| 作者 | Li BF(李冰锋) ; Tang YD(唐延东); Han Z(韩志)
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| 刊名 | MATHEMATICAL PROBLEMS IN ENGINEERING
 |
| 出版日期 | 2016 |
| 卷号 | 2016页码:1-14 |
| ISSN号 | 1024-123X |
| 产权排序 | 1 |
| 通讯作者 | 李冰锋 |
| 中文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology ; Physical Sciences |
| 类目[WOS] | Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications |
| 研究领域[WOS] | Engineering ; Mathematics |
| 关键词[WOS] | FACE RECOGNITION ; REPRESENTATION ; MANIFOLD ; OBJECTS ; PARTS |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000379478900001 |
| 源URL | [http://ir.sia.cn/handle/173321/18828]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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