OPML: A One-Pass Closed-Form Solution for Online Metric Learning

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 onepass 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 deeplylearned). 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. Disciplines Engineering | Science and Technology Studies Publication Details Li, W., Gao, Y., Wang, L., Zhou, L., Huo, J. & Shi, Y. (2017). OPML: A one-pass closed-form solution for online metric learning. Pattern Recognition, 75 302-31 Authors Wenbin Li, Yang Gao, Lei Wang, Luping Zhou, Jing Huo, and Yinghuan Shi This journal article is available at Research Online: http://ro.uow.edu.au/eispapers1/152 ar X iv :1 60 9. 09 17 8v 1 [c s. LG ] 29 S ep 2 01 6 1 OPML: A One-Pass Closed-Form Solution for Online Metric Learning Wenbin Li 1, Yang Gao1, Lei Wang2, Luping Zhou2, Jing Huo1, and Yinghuan Shi 1 1National Key Laboratory for Novel Software Technology, Nan jing University, China 2School of Computing and Information Technology, Universit y of Wollongong, Australia Abstract—To achieve a low computational cost when performing online metric learning for large-scale data, we present a onepass closed-form solution namely OPML in this paper. Typically, the proposed OPML first adopts a one-pass triplet constructi on 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)) 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 meth ods (OPML and COPML) on three typical tasks, including UCI data classification, face verification, and abnormal event detec tion 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 anddeeplylearned). 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 settin g is experimentally verified.