Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

By: 
Cong Ye; Konstantinos Slavakis; Johan Nakuci; Sarah F. Muldoon; John Medaglia

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds through the Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: landmark points are first identified in a non-random way to reveal the underlying geometric information of the feature point-cloud, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information on tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework is validated by extensive numerical tests against several state-of-the-art manifold-learning and (brain-)network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electro-encephalogram (EEG) data.

SPS on Twitter

  • SPS WEBINAR: Join us on Tuesday, 2 August for a new SPS Webinar, when Dr. Yue Li presents "Learning a Convolutional… https://t.co/Eps90ySYzq
  • Registration for ICIP 2021 is now open! This hybrid event will take place 19-22 September, with the in-person compo… https://t.co/s3kiGP4EPh
  • The Brain Space Initiative Talk Series continues on Friday, 30 July when Dr. Ioulia Kovelman presents "The Bilingua… https://t.co/6EqwqmBD0Q
  • There’s still time to register your team to win the US$5,000 grand prize in the 5-Minute Video Clip Contest, “Autom… https://t.co/76kh4jeL6i
  • Join the SPS Vizag Bay, Long Island, and Finland Chapters for the Seasonal School on Signal Processing and Communic… https://t.co/l04xac8qP5

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar