Generalized sparse metric learning with relative comparisons
文献类型：期刊论文
| 作者 | Huang, Kaizhu1; Ying, Yiming2; Campbell, Colin3 |
| 刊名 | KNOWLEDGE AND INFORMATION SYSTEMS
 |
| 出版日期 | 2011-07-01 |
| 卷号 | 28期号:1页码:25-45 |
| 关键词 | Generalized framework Metric learning Sparse |
| 英文摘要 | The objective of sparse metric learning is to learn a distance measure from a set of data in addition to finding a low-dimensional representation. Despite demonstrated success, the performance of existing sparse metric learning approaches is usually limited because the methods assumes certain problem relaxations or they target the SML objective indirectly. In this paper, we propose a Generalized Sparse Metric Learning method. This novel framework offers a unified view for understanding many existing sparse metric learning algorithms including the Sparse Metric Learning framework proposed in (Rosales and Fung ACM International conference on knowledge discovery and data mining (KDD), pp 367-373, 2006), the Large Margin Nearest Neighbor (Weinberger et al. in Advances in neural information processing systems (NIPS), 2006; Weinberger and Saul in Proceedings of the twenty-fifth international conference on machine learning (ICML-2008), 2008), and the D-ranking Vector Machine (D-ranking VM) (Ouyang and Gray in Proceedings of the twenty-fifth international conference on machine learning (ICML-2008), 2008). Moreover, GSML also establishes a close relationship with the Pairwise Support Vector Machine (Vert et al. in BMC Bioinform, 8, 2007). Furthermore, the proposed framework is capable of extending many current non-sparse metric learning models to their sparse versions including Relevant Component Analysis (Bar-Hillel et al. in J Mach Learn Res, 6:937-965, 2005) and a state-of-the-art method proposed in (Xing et al. Advances in neural information processing systems (NIPS), 2002). We present the detailed framework, provide theoretical justifications, build various connections with other models, and propose an iterative optimization method, making the framework both theoretically important and practically scalable for medium or large datasets. Experimental results show that this generalized framework outperforms six state-of-the-art methods with higher accuracy and significantly smaller dimensionality for seven publicly available datasets. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Information Systems |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | DIMENSIONALITY REDUCTION ; MACHINE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000292047400002 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3063]  |
| 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 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Exeter, Sch Engn Comp & Math, Exeter EX4 4QF, Devon, England 3.Univ Bristol, Dept Engn Math, Bristol BS8 1TR, Avon, England |
| 推荐引用方式 GB/T 7714 | Huang, Kaizhu,Ying, Yiming,Campbell, Colin. Generalized sparse metric learning with relative comparisons[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2011,28(1):25-45. |
| APA | Huang, Kaizhu,Ying, Yiming,&Campbell, Colin.(2011).Generalized sparse metric learning with relative comparisons.KNOWLEDGE AND INFORMATION SYSTEMS,28(1),25-45. |
| MLA | Huang, Kaizhu,et al."Generalized sparse metric learning with relative comparisons".KNOWLEDGE AND INFORMATION SYSTEMS 28.1(2011):25-45. |
入库方式： OAI收割
来源：自动化研究所
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