Matches in SemOpenAlex for { <https://semopenalex.org/work/W4235138536> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W4235138536 abstract "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 according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, 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<br> (EEG) data." @default.
- W4235138536 created "2022-05-12" @default.
- W4235138536 creator A5010926217 @default.
- W4235138536 creator A5015567863 @default.
- W4235138536 creator A5049952278 @default.
- W4235138536 creator A5057730293 @default.
- W4235138536 creator A5078701589 @default.
- W4235138536 date "2020-07-28" @default.
- W4235138536 modified "2023-09-27" @default.
- W4235138536 title "Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks" @default.
- W4235138536 doi "https://doi.org/10.36227/techrxiv.12725369" @default.
- W4235138536 hasPublicationYear "2020" @default.
- W4235138536 type Work @default.
- W4235138536 citedByCount "0" @default.
- W4235138536 crossrefType "posted-content" @default.
- W4235138536 hasAuthorship W4235138536A5010926217 @default.
- W4235138536 hasAuthorship W4235138536A5015567863 @default.
- W4235138536 hasAuthorship W4235138536A5049952278 @default.
- W4235138536 hasAuthorship W4235138536A5057730293 @default.
- W4235138536 hasAuthorship W4235138536A5078701589 @default.
- W4235138536 hasBestOaLocation W42351385361 @default.
- W4235138536 hasConcept C109546454 @default.
- W4235138536 hasConcept C11413529 @default.
- W4235138536 hasConcept C114614502 @default.
- W4235138536 hasConcept C12520029 @default.
- W4235138536 hasConcept C134306372 @default.
- W4235138536 hasConcept C153180895 @default.
- W4235138536 hasConcept C154945302 @default.
- W4235138536 hasConcept C169391604 @default.
- W4235138536 hasConcept C181104567 @default.
- W4235138536 hasConcept C184720557 @default.
- W4235138536 hasConcept C195065555 @default.
- W4235138536 hasConcept C202444582 @default.
- W4235138536 hasConcept C2524010 @default.
- W4235138536 hasConcept C2779593128 @default.
- W4235138536 hasConcept C33923547 @default.
- W4235138536 hasConcept C41008148 @default.
- W4235138536 hasConcept C73555534 @default.
- W4235138536 hasConcept C74193536 @default.
- W4235138536 hasConceptScore W4235138536C109546454 @default.
- W4235138536 hasConceptScore W4235138536C11413529 @default.
- W4235138536 hasConceptScore W4235138536C114614502 @default.
- W4235138536 hasConceptScore W4235138536C12520029 @default.
- W4235138536 hasConceptScore W4235138536C134306372 @default.
- W4235138536 hasConceptScore W4235138536C153180895 @default.
- W4235138536 hasConceptScore W4235138536C154945302 @default.
- W4235138536 hasConceptScore W4235138536C169391604 @default.
- W4235138536 hasConceptScore W4235138536C181104567 @default.
- W4235138536 hasConceptScore W4235138536C184720557 @default.
- W4235138536 hasConceptScore W4235138536C195065555 @default.
- W4235138536 hasConceptScore W4235138536C202444582 @default.
- W4235138536 hasConceptScore W4235138536C2524010 @default.
- W4235138536 hasConceptScore W4235138536C2779593128 @default.
- W4235138536 hasConceptScore W4235138536C33923547 @default.
- W4235138536 hasConceptScore W4235138536C41008148 @default.
- W4235138536 hasConceptScore W4235138536C73555534 @default.
- W4235138536 hasConceptScore W4235138536C74193536 @default.
- W4235138536 hasLocation W42351385361 @default.
- W4235138536 hasLocation W42351385362 @default.
- W4235138536 hasLocation W42351385363 @default.
- W4235138536 hasOpenAccess W4235138536 @default.
- W4235138536 hasPrimaryLocation W42351385361 @default.
- W4235138536 hasRelatedWork W2010954169 @default.
- W4235138536 hasRelatedWork W2076520961 @default.
- W4235138536 hasRelatedWork W2103444992 @default.
- W4235138536 hasRelatedWork W2110459882 @default.
- W4235138536 hasRelatedWork W2118043379 @default.
- W4235138536 hasRelatedWork W2141018987 @default.
- W4235138536 hasRelatedWork W2151022383 @default.
- W4235138536 hasRelatedWork W2603933437 @default.
- W4235138536 hasRelatedWork W2765538321 @default.
- W4235138536 hasRelatedWork W2787871992 @default.
- W4235138536 isParatext "false" @default.
- W4235138536 isRetracted "false" @default.
- W4235138536 workType "article" @default.