Zhou, S., Li, W., Cox, C.R. et al. (1 more author) (2020) Side information dependence as a regularizer for analyzing human brain conditions across cognitive experiments. In: Proceedings of the AAAI Conference on Artificial Intelligence. 34th AAAI Conference on Artificial Intelligence, 07-12 Feb 2020, New York, USA. AAAI Press , pp. 6957-6964. ISBN 9781577358350
Abstract
The increasing of public neuroimaging datasets opens a door to analyzing homogeneous human brain conditions across datasets by transfer learning (TL). However, neuroimaging data are high-dimensional, noisy, and with small sample sizes. It is challenging to learn a robust model for data across different cognitive experiments and subjects. A recent TL approach minimizes domain dependence to learn common cross-domain features, via the Hilbert-Schmidt Independence Criterion (HSIC). Inspired by this approach and the multi-source TL theory, we propose a Side Information Dependence Regularization (SIDeR) learning framework for TL in brain condition decoding. Specifically, SIDeR simultaneously minimizes the empirical risk and the statistical dependence on the domain side information, to reduce the theoretical generalization error bound. We construct 17 brain decoding TL tasks using public neuroimaging data for evaluation. Comprehensive experiments validate the superiority of SIDeR over ten competing methods, particularly an average improvement of 15.6% on the TL tasks with multi-source experiments.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Association for the Advancement of Artificial Intelligence. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/R014507/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Jan 2020 10:49 |
Last Modified: | 10 Sep 2020 15:44 |
Status: | Published |
Publisher: | AAAI Press |
Refereed: | Yes |
Identification Number: | 10.1609/aaai.v34i04.6179 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154983 |