| Topographic NMF for Data Representation | |
Xiao, Yanhui1,2; Zhu, Zhenfeng1,2; Zhao, Yao1; Wei, Yunchao1,2; Wei, Shikui1,2; Li, Xuelong3
| |
| 作者部门 | 光学影像学习与分析中心 |
| 2014-10-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| ISSN | 2168-2267 |
| 卷号 | 44期号:10页码:1762-1771 |
| 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, |
| 文章类型 | Article |
| 关键词 | Data Clustering Dimension Reduction Feature Invariance Machine Learning Nonnegative Matrix Factorization |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCYB.2013.2294215 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | NONNEGATIVE MATRIX FACTORIZATION ; RECOGNITION ; PARTS ; OBJECTS |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000342228100005 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/22369 |
| 专题 | 光谱成像技术研究室 |
| 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. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Topographic NMF for （1137KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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。
修改评论