OPML: A one-pass closed-form solution for online metric learning

PATTERN RECOGNITION

卷:75

Article

Li, Wenbin¹  Gao, Yang¹  Wang, Lei²  Zhou, Luping²  Huo, Jing¹  Shi, Yinghuan¹ 

[1] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China

[2] Univ Wollongong, Sch Comp & Informat Technol, Wollongong, NSW, Australia

关键词: One-pass;    Online metric learning;    Triplet construction;    Face verification;    Abnormal event detection;   

DOI  :  10.1016/j.patcog.2017.03.016

来源: Elsevier

【 摘 要 】

To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper. Typically, the proposed OPML first adopts a one-pass triplet construction strategy, which aims to use only a very small number of triplets to approximate the representation ability of whole original triplets obtained by batch-manner methods. Then, OPML employs a closed-form solution to update the metric for new coming samples, which leads to a low space (i.e., O(d)) and time (i.e., O(d(2))) complexity, where d is the feature dimensionality. In addition, an extension of OPML (namely COPML) is further proposed to enhance the robustness when in real case the first several samples come from the same class (i.e., cold start problem). In the experiments, we have systematically evaluated our methods (OPML and COPML) on three typical tasks, including UCI data classification, face verification, and abnormal event detection in videos, which aims to fully evaluate the proposed methods on different sample number, different feature dimensionalities and different feature extraction ways (i.e., hand-crafted and deeply-learned). The results show that OPML and COPML can obtain the promising performance with a very low computational cost. Also, the effectiveness of COPML under the cold start setting is experimentally verified. (C) 2017 Elsevier Ltd. All rights reserved.

【 授权许可】

Free