Topographic NMF for Data Representation
文献类型：期刊论文
| 作者 | Xiao, Yanhui1,2; Zhu, Zhenfeng1,2; Zhao, Yao1; Wei, Yunchao1,2; Wei, Shikui1,2; Li, Xuelong3
|
| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 2014-10-01 |
| 卷号 | 44期号:10页码:1762-1771 |
| 关键词 | Data clustering dimension reduction feature invariance machine learning nonnegative matrix factorization |
| ISSN号 | 2168-2267 |
| 英文摘要 | nonnegative matrix factorization (nmf) is a useful technique to explore a parts-based representation by decomposing the original data matrix into a few parts-based basis vectors and encodings with nonnegative constraints. it has been widely used in image processing and pattern recognition tasks due to its psychological and physiological interpretation of natural data whose representation may be parts-based in human brain. however, the nonnegative constraint for matrix factorization is generally not sufficient to produce representations that are robust to local transformations. to overcome this problem, in this paper, we proposed a topographic nmf (tnmf), which imposes a topographic constraint on the encoding factor as a regularizer during matrix factorization. in essence, the topographic constraint is a two-layered network, which contains the square nonlinearity in the first layer and the square-root nonlinearity in the second layer. by pooling together the structure-correlated features belonging to the same hidden topic, the tnmf will force the encodings to be organized in a topographical map. thus, the feature invariance can be promoted. some experiments carried out on three standard datasets validate the effectiveness of our method in comparison to the state-of-the-art approaches. index terms-data clustering, dimension reduction, |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; computer science, cybernetics |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | nonnegative matrix factorization ; recognition ; parts ; objects |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000342228100005 |
| 公开日期 | 2015-03-18 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/22369]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing, Peoples R China 2.Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiao, Yanhui,Zhu, Zhenfeng,Zhao, Yao,et al. Topographic NMF for Data Representation[J]. ieee transactions on cybernetics,2014,44(10):1762-1771. |
| APA | Xiao, Yanhui,Zhu, Zhenfeng,Zhao, Yao,Wei, Yunchao,Wei, Shikui,&Li, Xuelong.(2014).Topographic NMF for Data Representation.ieee transactions on cybernetics,44(10),1762-1771. |
| MLA | Xiao, Yanhui,et al."Topographic NMF for Data Representation".ieee transactions on cybernetics 44.10(2014):1762-1771. |
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。