Geometry Preserving Multi-task Metric Learning
文献类型：会议论文
| 作者 | Yang, Peipei ; Huang, Kaizhu; Liu, Cheng-Lin
|
| 出版日期 | 2012-09 |
| 会议日期 | 2012-9-23 |
| 会议地点 | Bristol, UK |
| 关键词 | Multi-task Learning Metric Learning Geometry Preserving |
| DOI | 10.1007/978-3-642-33460-3_47 |
| 英文摘要 | Multi-task learning has been widely studied in machine learning due to its capability to improve the performance of multiple related learning problems. However, few researchers have applied it on the important metric learning problem. In this paper, we propose to couple multiple related metric learning tasks with 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 regularizer, the new multi-task metric learning 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 (PG) and show that our framework leads to a larger PG in theory. In addition, our formulation proves to be jointly convex and the global optimal solution can be guaranteed. A series of experiments across very different disciplines verify that our proposed algorithm can consistently outperform the current methods. |
| 会议录 | Machine Learning and Knowledge Discovery in Databases
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12500]  |
| 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 |
| 通讯作者 | Huang, Kaizhu |
| 作者单位 | National Laboratory of Pattern Recognition |
| 推荐引用方式 GB/T 7714 | Yang, Peipei,Huang, Kaizhu,Liu, Cheng-Lin. Geometry Preserving Multi-task Metric Learning[C]. 见:. Bristol, UK. 2012-9-23. |
入库方式： OAI收割
来源：自动化研究所
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。