Paper Info
Title
Parametric Transfer Learning Based on the Fisher Divergence for Well-Being Prediction
Abstract
Smartphones and wearable sensors are increasingly used for personalised prediction and management in healthcare contexts. Personalisation requires tuning/learning a model of the user. However, traditional machine learning approaches for personalised modelling typically require the availability of sufficient personal data of a suitable nature for training, which can be a challenge in such contexts. We propose a parametric transfer learning approach based on the Fisher divergence to address this challenge. This makes it possible to create patient-specific models and make predictions of self-reported well-being scores, when training is performed incrementally on sparse data becoming slowly available over time. This approach allows us to make informed predictions even in the early stages of data collection, by leveraging external information coming from other patients, in the form of a prior used within a Markov-Chain Monte Carlo process. Our approach performs favourably against competing models and standard baselines, particularly when long-term forecasts are required but training data cover only a short period.
Year
DOI
Venue
2019
10.1109/BIBE.2019.00059
2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
Keywords
Field
DocType
Transfer Learning, MCMC, Bayesian inference, Well being prediction, personalised modelling
Data collection,Bayesian inference,Markov chain Monte Carlo,Wearable computer,Computer science,Transfer of learning,Parametric statistics,Artificial intelligence,Sparse matrix,Machine learning,Personalization
Conference
ISSN
ISBN
Citations 
2159-5410
978-1-7281-4618-8
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Eirini Christinaki100.34
Tasos Papastylianou200.34
Riccardo Poli32589308.79
luca citi416827.88