Name
Abstract | ||
|---|---|---|
This paper advocates Riemannian multi-manifold modeling for network-wide time-series analysis: Dynamic brainnetwork data yield features which are viewed as points in or close to a union of a finite number of submanifolds of a Riemannian manifold. Distinguishing disparate time series amounts then to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for network... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSIPN.2017.2774504 | IEEE Transactions on Signal and Information Processing over Networks |
Keywords | Field | DocType |
Time series analysis,Manifolds,Feature extraction,Clustering algorithms,Information processing,Correlation | Information geometry,Topology,Scalar curvature,Riemannian manifold,Computer science,Cluster analysis,Riemannian geometry,Fundamental theorem of Riemannian geometry,Exponential map (Riemannian geometry),Manifold | Journal |
Volume | Issue | ISSN |
4 | 3 | 2373-776X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinos Slavakis | 1 | 583 | 40.76 |
| Shiva Salsabilian | 2 | 0 | 0.34 |
| David S. Wack | 3 | 10 | 2.93 |
| sarah feldt muldoon | 4 | 17 | 4.85 |
| Henry E. Baidoo-Williams | 5 | 28 | 4.89 |
| Jean Vettel | 6 | 74 | 9.58 |
| Matthew Cieslak | 7 | 76 | 5.99 |
| Scott T. Grafton | 8 | 432 | 45.40 |