Subspace clustering guided convex nonnegative matrix factorization
文献类型：期刊论文
| 作者 | Cui, Guosheng1,2; Li, Xuelong1; Dong, Yongsheng1; Dong, YS (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China. |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2018-05-31 |
| 卷号 | 292页码:38-48 |
| 关键词 | Convex Nonnegative Matrix Factorization Subspace Clustering Multiple Centroids Geometry Structure Image Clustering |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2018.02.067 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | As one of the most important information of the data, the geometry structure information is usually modeled by a similarity graph to enforce the effectiveness of nonnegative matrix factorization (NMF). However, pairwise distance based graph is sensitive to noise and can not capture the subspace structure of the data. Reconstruction coefficients based graph can capture the subspace structure of the data, but the procedure of building the representation based graph is usually independent to the framework of NMF. To address this issue, a novel subspace clustering guided convex nonnegative matrix factorization (SC-CNMF) is proposed. In this NMF framework, the nonnegative subspace clustering is incorporated to learning the representation based graph, and meanwhile, a convex nonnegative matrix factorization is also updated simultaneously. To tackle the noise influence of the dataset, only k largest entries of each representation are kept in the subspace clustering. To capture the complicated geometry structure of the data, multiple centroids are also introduced to describe each cluster. Additionally, a row constraint is used to remove the relevance among the rows of the encoding matrix, which can help to improve the clustering performance of the proposed model. For the proposed NMF framework, two different objective functions with different optimizing schemes are designed. Image clustering experiments are conducted to demonstrate the effectiveness of the proposed methods on several datasets and compared with some related works based on NMF together with k-means clustering method and PCA as baseline. (c) 2018 Elsevier B.V. All rights reserved. |
| 学科主题 | Computer Science, Artificial Intelligence |
| WOS关键词 | REPRESENTATION |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000429321400003 |
| 资助机构 | National Natural Science Foundation of China(61761130079 ; Key Research Program of Frontier Sciences, CAS(QYZDY-SSW-JSC044) ; Training Program for the Young-Backbone Teachers in Universities of Henan Province(2017GGJS065) ; State Key Laboratory of Virtual Reality Technology and Systems(BUAAVR-16KF-04) ; U1604153) |
| 源URL | [http://ir.opt.ac.cn/handle/181661/30050]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 通讯作者 | Dong, YS (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China. |
| 作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China 2.Univ Chinese Acad Sci, 19A Yuquanlu, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cui, Guosheng,Li, Xuelong,Dong, Yongsheng,et al. Subspace clustering guided convex nonnegative matrix factorization[J]. NEUROCOMPUTING,2018,292:38-48. |
| APA | Cui, Guosheng,Li, Xuelong,Dong, Yongsheng,&Dong, YS .(2018).Subspace clustering guided convex nonnegative matrix factorization.NEUROCOMPUTING,292,38-48. |
| MLA | Cui, Guosheng,et al."Subspace clustering guided convex nonnegative matrix factorization".NEUROCOMPUTING 292(2018):38-48. |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。