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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
Department机器人学研究室
Conference Name2018 24th International Conference on Pattern Recognition (ICPR)
Conference DateAugust 20-24, 2018
Conference PlaceBeijing, China
Source Publication2018 24th International Conference on Pattern Recognition (ICPR)
PublisherIEEE
Publication PlaceNew York
2018
Pages207-212
Indexed ByEI ; CPCI(ISTP)
EI Accession number20190206369065
WOS IDWOS:000455146800035
Contribution Rank1
ISSN1051-4651
ISBN978-1-5386-3788-3
AbstractRecently, 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
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23854
Collection机器人学研究室
Corresponding AuthorSun G(孙干)
Affiliation1.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.
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