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 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). Specifi-
cally, 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