Learning to compare with few data for personalised human activity recognition.

Wiratunga, Nirmalie; Wijekoon, Anjana; Cooper, Kay

Abstract

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist view of learning-to-learn, such that models are rapidly transferable to related (but different) new tasks. Unlike task-specific model training, a meta-learner’s training instance - referred to as a meta-instance - is a composite of two sets: a support set and a query set of instances. In our work, we introduce learning-to-learn personalised models from few data. We motivate our contribution through an application where personalisation plays an important role, mainly that of human activity recognition for self-management of chronic diseases. We extend the meta-instance creation process where random sampling of support and query sets is carried out on a reduced sample conditioned by a domain-specific attribute; namely the person or user, in order to create meta-instances for personalised HAR. Our meta-learning for personalisation is compared with several state-of-the-art meta-learning strategies: 1) matching network (MN), which learns an embedding for a metric function; 2) relation network (RN) that learns to predict similarity between paired instances; and 3) MAML, a model-agnostic machine-learning algorithm that optimizes the model parameters for rapid adaptation. Results confirm that personalised meta-learning significantly improves performance over non personalised meta-learners.

Citation

WIRATUNGA, N., WIJEKOON, A. and COOPER, K. 2020. Learning to compare with few data for personalised human activity recognition. In Watson, I and Weber, R. (eds.) Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference]. Lecture notes in computer science, 12311. Cham: Springer [online], pages 3-14. Available from: https://doi.org/10.1007/978-3-030-58342-2_1

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 28th International conference on case-based reasoning research and development (ICCBR 2020)
• Start Date: Jun 8, 2020
• End Date: Jun 12, 2020
• Acceptance Date: Apr 14, 2020
• Online Publication Date: Oct 3, 2020
• Publication Date: Oct 31, 2020
• Deposit Date: Jun 15, 2020
• Publicly Available Date: Jun 15, 2020
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 12311
• Pages: 3-14
• Series Title: Lecture notes in computer science
• Series Number: 12311
• Series ISSN: 0302-9743
• Book Title: Case-based resoning research and development: proceedings of the 28th International conference on case-based reasoning research and development 2020 (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference].
• ISBN: 9783030583415
• DOI: https://doi.org/10.1007/978-3-030-58342-2_1
• Keywords: Artificial intelligence; Machine learning; Meta-learning; Personalised meta-learning; Case-based reasoning
• Public URL: https://rgu-repository.worktribe.com/output/932917