Geometry preserving multi-task metric learning
文献类型：期刊论文
| 作者 | Yang, Peipei ; Huang, Kaizhu; Liu, Cheng-Lin
|
| 刊名 | MACHINE LEARNING
 |
| 出版日期 | 2013-07-01 |
| 卷号 | 92期号:1页码:133-175 |
| 关键词 | Multi-task learning Metric learning Geometry preserving von Neumann divergence Bregman matrix divergence |
| 英文摘要 | In this paper, we consider the multi-task metric learning problem, i.e., the problem of learning multiple metrics from several correlated tasks simultaneously. Despite the importance, there are only a limited number of approaches in this field. While the existing methods often straightforwardly extend existing vector-based methods, we propose to couple multiple related metric learning tasks with the von Neumann divergence. On one hand, the novel regularized approach extends previous methods from the vector regularization to a general matrix regularization framework; on the other hand and more importantly, by exploiting von Neumann divergence as the regularization, the new multi-task metric learning method has the capability to well preserve the data geometry. This leads to more appropriate propagation of side-information among tasks and provides potential for further improving the performance. We propose the concept of geometry preserving probability and show that our framework encourages a higher geometry preserving probability in theory. In addition, our formulation proves to be jointly convex and the global optimal solution can be guaranteed. We have conducted extensive experiments on six data sets (across very different disciplines), and the results verify that our proposed approach can consistently outperform almost all the current methods. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | BREGMAN DIVERGENCES ; ENTROPY ; OPTIMIZATION |
| 收录类别 | SCI ; ISTP |
| 语种 | 英语 |
| WOS记录号 | WOS:000321273100006 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3081]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 作者单位 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Peipei,Huang, Kaizhu,Liu, Cheng-Lin. Geometry preserving multi-task metric learning[J]. MACHINE LEARNING,2013,92(1):133-175. |
| APA | Yang, Peipei,Huang, Kaizhu,&Liu, Cheng-Lin.(2013).Geometry preserving multi-task metric learning.MACHINE LEARNING,92(1),133-175. |
| MLA | Yang, Peipei,et al."Geometry preserving multi-task metric learning".MACHINE LEARNING 92.1(2013):133-175. |
入库方式： OAI收割
来源：自动化研究所
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