Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves
文献类型：期刊论文
| 作者 | Li, Chun-Guo1,2; Mei, Xing1 ; Hu, Bao-Gang1
|
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2015-12-01 |
| 卷号 | 27期号:12页码:3404-3416 |
| 关键词 | Unsupervised ranking multi-attribute meta-rules data skeleton principal curves Bezier curves |
| 英文摘要 | Unsupervised ranking faces one critical challenge in evaluation applications, that is, no ground truth is available. When PageRank and its variants show a good solution in related objects, they are applicable only for ranking from link-structure data. In this work, we focus on unsupervised ranking from multi-attribute data which is also common in evaluation tasks. To overcome the challenge, we propose five essential meta-rules for the design and assessment of unsupervised ranking approaches: scale and translation invariance, strict monotonicity, compatibility of linearity and nonlinearity, smoothness, and explicitness of parameter size. These meta-rules are regarded as high level knowledge for unsupervised ranking tasks. Inspired by the works in [ 12] and [ 35], we propose a ranking principal curve (RPC) model, which learns a one-dimensional manifold function to perform unsupervised ranking tasks on multi-attribute observations. Furthermore, the RPC is modeled to be a cubic Bezier curve with control points restricted in the interior of a hypercube, complying with all the five meta-rules to infer a reasonable ranking list. With control points as model parameters, one is able to understand the learned manifold and to interpret and visualize the ranking results. Numerical experiments of the presented RPC model are conducted on two open datasets of different ranking applications. In comparison with the state-of-the-art approaches, the new model is able to show more reasonable ranking lists. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | DESIGN |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000364853800019 |
| 公开日期 | 2016-02-26 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10510]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Hebei Univ, Coll Math & Informat Sci, Baoding 071002, Hebei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Chun-Guo,Mei, Xing,Hu, Bao-Gang. Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2015,27(12):3404-3416. |
| APA | Li, Chun-Guo,Mei, Xing,&Hu, Bao-Gang.(2015).Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,27(12),3404-3416. |
| MLA | Li, Chun-Guo,et al."Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 27.12(2015):3404-3416. |
入库方式： OAI收割
来源：自动化研究所
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