Constrained Metric Learning via Distance Gap Maximization
文献类型：会议论文
| 作者 | Wei Liu; Xinmei Tian; Dacheng Tao; Jianzhuang Liu |
| 出版日期 | 2010 |
| 会议名称 | 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 |
| 英文摘要 | Vectored data frequently occur in a variety of fields, which are easy to handle since they can be mathematically abstracted as points residing in a Euclidean space. An appropriate distance metric in the data space is quite demanding for a great number of applications. In this paper, we pose robust and tractable metric learning under pairwise constraints that are expressed as similarity judgements between data pairs. The major features of our approach include: 1) it maximizes the gap between the average squared distance among dissimilar pairs and the average squared distance among similar pairs; 2) it is capable of propagating similar constraints to all data pairs; and 3) it is easy to implement in contrast to the existing approaches using expensive optimization such as semidefi-nite programming. Our constrained metric learning approach has widespread applicability without being limited to particular backgrounds. Quantitative experiments are performed for classification and retrieval tasks, uncovering the effectiveness of the proposed approach. Copyright 漏 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/2757]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2010 |
| 推荐引用方式 GB/T 7714 | Wei Liu,Xinmei Tian,Dacheng Tao,et al. Constrained Metric Learning via Distance Gap Maximization[C]. 见:24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10. |
入库方式： OAI收割
来源：深圳先进技术研究院
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