Conference Proceeding Article
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 realworld 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 learningalgorithm 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.
Artificial intelligence, E-learning, Active-learning algorithm, Learning approach, Online learning algorithms, Real-world datasets, Similarity functions, Similarity learning, Specific tasks, Theoretical guarantees, Learning algorithms
Artificial Intelligence and Robotics | Computer Engineering
Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 2017 August 19-25
City or Country
HAO, Shuji; ZHAO, Peilin; LIU, Yong; HOI, Steven C. H.; and MIAO, Chunyan.
Online multitask relative similarity learning. (2017). Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 2017 August 19-25. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3846
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.