Online Multitask Relative Similarity Learning

Online Multitask Relative Similarity Learning
Title:
Online Multitask Relative Similarity Learning
Other Titles:
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI)
Publication Date:
19 August 2017
Citation:
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.
License type:
PublisherCopyrights
Funding Info:
Description:
ISBN:

Files uploaded:

File Size Format Action
ijcai-0253.pdf 203.03 KB PDF Open