Jia, S, Li, X, Huang, T et al. (2 more authors) (2022) Representing the dynamics of high-dimensional data with non-redundant wavelets. Patterns, 3 (3). 100424. ISSN 2666-3899
Abstract
A crucial question in data science is to extract meaningful information embedded in high-dimensional data into a low-dimensional set of features that can represent the original data at different levels. Wavelet analysis is a pervasive method for decomposing time-series signals into a few levels with detailed temporal resolution. However, obtained wavelets are intertwined and over-represented across levels for each sample and across different samples within one population. Here, using neuroscience data of simulated spikes, experimental spikes, calcium imaging signals, and human electrocorticography signals, we leveraged conditional mutual information between wavelets for feature selection. The meaningfulness of selected features was verified to decode stimulus or condition with high accuracy yet using only a small set of features. These results provide a new way of wavelet analysis for extracting essential features of the dynamics of spatiotemporal neural data, which then enables to support novel model design of machine learning with representative features.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | neural coding; wavelet analysis; mutual information; conditional information; feature selection; dimensionality reduction; neural spikes; calcium imaging; ECoG |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Mar 2022 12:51 |
Last Modified: | 24 Feb 2025 10:44 |
Status: | Published |
Publisher: | Cell Press (Elsevier) |
Identification Number: | 10.1016/j.patter.2021.100424 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184219 |