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Journal article
Network clustering via kernel-ARMA modeling and the Grassmannian: The brain-network case
Signal processing, v 179, 107834
Feb 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
•Proposed framework can tackle all possible clustering tasks in dynamic (brain) networks.•Latent non-linear and causal dependencies are captured by kernel (vector-valued) autoregressive-moving-average model.•Clustering algorithm exploit the underlying Riemannian geometry without the need to know the number of clusters a-priori.•Tests on synthetic and real data show favorable performance of the proposed scheme over state-of-the-art methods.
This paper demonstrates that all clustering tasks in a dynamic (brain) network, i.e., state clustering, community detection, and subnetwork state-sequence clustering, can be addressed by a novel unifying network-clustering framework. The connecting threads of the components of the proposed framework are: a novel kernel-based autoregressive-moving-average (ARMA) model which propels feature extraction from the network time-series, and the Riemannian geometry of the Grassmann manifold (Grassmannian) into which the extracted features are mapped. Clustering of the Grassmannian features is performed via the novel extension of a recently introduced algorithm which capitalizes on the Grassmannian distances and angular information of the point-cloud of features. Numerical tests on synthetic and real functional-magnetic-resonance-imaging (fMRI) data showcase the favorable performance of the proposed scheme against state-of-the-art network-clustering and manifold-learning methods, and corroborate the claim of this paper that the proposed framework can serve as a useful data-analytic toolbox for network(-neuroscience) research.
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Details
- Title
- Network clustering via kernel-ARMA modeling and the Grassmannian: The brain-network case
- Creators
- Cong Ye - State University of New YorkKonstantinos Slavakis - State University of New YorkPratik V. Patil - State University of New YorkJohan Nakuci - University at Buffalo, State University of New YorkSarah F. Muldoon - University at Buffalo, State University of New YorkJohn Medaglia - Drexel University
- Publication Details
- Signal processing, v 179, 107834
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000601320000019
- Scopus ID
- 2-s2.0-85092398600
- Other Identifier
- 991019168915004721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic