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Online Multitask Relative Similarity Learning


Authors: S. Hao, P. Zhao, Y. Liu, S. C. H. Hoi, and C. Miao
Title: Online Multitask Relative Similarity Learning
Abstract: Relative similarity learning~(RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real-world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose an active learning algorithm to save the labeling cost. The proposed algorithms not only enjoy theoretical guarantee, but also show high efficacy and efficiency in extensive experiments on real-world datasets.
Keywords: 
Conference Name: 26th International Joint Conference on Artificial Intelligence (IJCAI'17)
Location: Melbourne, Australia
Publisher: AAAI Press
Year: 2017
Accepted PDF File: Online_Multitask_Relative_Similarity_Learning_accepted.pdf
Permanent Link: https://dx.doi.org/10.24963/ijcai.2017/253
Reference: S. Hao, P. Zhao, Y. Liu, S. C. H. Hoi, and C. Miao, “Online multitask relative similarity learning,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17). AAAI Press, August 2017, pp. 1823–1829.
bibtex: 
@inproceedings{LILY-c128, 
    author = {Hao, Shuji and Zhao, Peilin and Liu, Yong and Hoi, Steven C. H. and Miao, Chunyan},
    title  = {Online Multitask Relative Similarity Learning},  
    booktitle = {Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17)}, 
    year  = {2017}, 
    month = {August}, 
    pages = {1823-1829}, 
    location = {Melbourne, Australia},
    publisher = {AAAI Press},
 }