Transfer Metric Learning: Algorithms, Applications and Outlooks

TL;DR

This survey reviews transfer metric learning (TML), highlighting its methods, applications, challenges, and future directions in leveraging knowledge from related domains to improve distance metric learning with limited labels.

Contribution

It systematically categorizes TML approaches, discusses their applications, and outlines future research challenges and directions.

Findings

TML effectively mitigates label scarcity in DML.

Various strategies like direct, subspace, and distribution approximation are used.

TML faces challenges in complex data and theoretical understanding.

Abstract

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data mining algorithms, and usually require large amount of label information (such as class labels or pair/triplet constraints) to achieve satisfactory performance. However, the label information may be insufficient in real-world applications due to the high-labeling cost, and DML may fail in this case. Transfer metric learning (TML) is able to mitigate this issue for DML in the domain of interest (target domain) by leveraging knowledge/information from other related domains (source domains). Although achieved a certain level of development, TML has limited success in various aspects such as selective transfer, theoretical understanding, handling complex data, big data and extreme cases. In this survey, we present a systematic review of the TML literature. In particular, we group TML into different categories according to different settings and metric transfer strategies, such as direct metric approximation, subspace approximation, distance approximation, and distribution approximation. A summarization and insightful discussion of the various TML approaches and their applications will be presented. Finally, we indicate some challenges and provide possible future directions.