| Online Low-Rank Metric Learning via Parallel Coordinate Descent Method | |
Sun G(孙干)1,2 ; Cong Y(丛杨)1; Wang Q(王强)1,2; Xu XW(徐晓伟)3
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| Department | 机器人学研究室 |
| Conference Name | 2018 24th International Conference on Pattern Recognition (ICPR) |
| Conference Date | August 20-24, 2018 |
| Conference Place | Beijing, China |
| Source Publication | 2018 24th International Conference on Pattern Recognition (ICPR) |
| Publisher | IEEE |
| Publication Place | New York |
| 2018 | |
| Pages | 207-212 |
| Indexed By | EI ; CPCI（ISTP） |
| EI Accession number | 20190206369065 |
| WOS ID | WOS:000455146800035 |
| Contribution Rank | 1 |
| ISSN | 1051-4651 |
| ISBN | 978-1-5386-3788-3 |
| Abstract | Recently, many machine learning problems rely on a valuable tool: metric learning. However, in many applications, large-scale applications embedded in high-dimensional feature space may induce both computation and storage requirements to grow quadratically. In order to tackle these challenges, in this paper, we intend to establish a robust metric learning formulation with the expectation that online metric learning and parallel optimization can solve large-scale and high-dimensional data efficiently, respectively. Specifically, based on the matrix factorization strategy, the first step aims to learn a similarity function in the objective formulation for similarity measurement; in the second step, we derive a variational trace norm to promote low-rankness on the transformation matrix. After converting this variational regularization into its separable form, for the model optimization, we present an parallel block coordinate descent method to learn the optimal metric parameters, which can handle the high-dimensional data in an efficient way. Crucially, our method shares the efficiency and flexibility of block coordinate descent method, and it is also guaranteed to converge to the optimal solution. Finally, we evaluate our approach by analyzing scene categorization dataset with tens of thousands of dimensions, and the experimental results show the effectiveness of our proposed model. |
| Language | 英语 |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.sia.cn/handle/173321/23854 |
| Collection | 机器人学研究室 |
| Corresponding Author | Sun G(孙干) |
| Affiliation | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China 2.University of Chinese Academy of Sciences, China 3.Department of Information Science, University of Arkansas at Little Rock, USA |
| Recommended Citation GB/T 7714 | Sun G,Cong Y,Wang Q,et al. Online Low-Rank Metric Learning via Parallel Coordinate Descent Method[C]. New York:IEEE,2018:207-212. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Online Low-rank Metr（555KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Download | ||
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