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Conference proceeding
Online Classification of Dynamic Multilayer-Network Time Series in Riemannian Manifolds
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), v 2021-, pp 3815-3819
06 Jun 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This work exploits Riemannian manifolds to introduce a geometric framework for online state and community classification in dynamic multilayer networks where nodes are annotated with time series. A bottom-up approach is followed, starting from the extraction of Riemannian features from nodal time series, and reaching up to on- line/sequential classification of features via geodesic distances and angular information in the tangent spaces of a Riemannian manifold. As a case study, features in the Grassmann manifold are generated by fitting a kernel autoregressive-moving-average model to the nodal time series of the multilayer network. The paper highlights also numerical tests on synthetic and real brain-network data, where it is shown that the proposed geometric framework outperforms state-of- the-art deep-learning models in classification accuracy, especially in cases where the number of training data is small with respect to the number of the testing ones.
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Details
- Title
- Online Classification of Dynamic Multilayer-Network Time Series in Riemannian Manifolds
- Creators
- Cong Ye - University at Buffalo, State University of New YorkKonstantinos Slavakis - University at Buffalo, State University of New YorkJohan Nakuci - Neuroscience Program,UB,SUNY,USASarah F. Muldoon - UB,Department of Mathematics and CDS&E Program,SUNY,USAJohn Medaglia - Drexel UniversityIEEE
- Publication Details
- ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), v 2021-, pp 3815-3819
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000704288404015
- Scopus ID
- 2-s2.0-85115056899
- Other Identifier
- 991019168806604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Acoustics
- Computer Science, Artificial Intelligence
- Computer Science, Software Engineering
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology