Online multitask relative similarity learning

• Main Authors: HAO, Shuji, ZHAO, Peilin, LIU, Yong, HOI, Steven C. H., MIAO, Chunyan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: 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; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3846; https://ink.library.smu.edu.sg/context/sis_research/article/4848/viewcontent/0253.pdf
• Institution: Singapore Management University

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.