Show simple item record

dc.contributor.authorGogineni, Vinay Chakravarthi
dc.contributor.authorDasanadoddi Venkategowda, Naveen Kumar
dc.contributor.authorWerner, Stefan
dc.date.accessioned2023-02-02T12:09:01Z
dc.date.available2023-02-02T12:09:01Z
dc.date.created2022-11-09T18:28:18Z
dc.date.issued2022
dc.identifier.isbn978-90-827970-9-1
dc.identifier.urihttps://hdl.handle.net/11250/3047994
dc.description.abstractThis paper presents a majorization-minimization-based framework for learning time-varying graphs from spatial-temporal measurements with non-convex penalties. The proposed approach infers time-varying graphs by using the log-likelihood function in conjunction with two non-convex regularizers. Using the log-likelihood function under a total positivity constraint, we can construct the Laplacian matrix from the off-diagonal elements of the precision matrix. Furthermore, we employ non-convex regularizer functions to constrain the changes in graph topology and associated weight evolution to be sparse. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods in sparse and non-sparse situations.en_US
dc.description.abstractDynamic Graph Topology Learning with Non-Convex Penaltiesen_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof30th European Signal Processing Conference (EUSIPCO 2022)
dc.titleDynamic Graph Topology Learning with Non-Convex Penaltiesen_US
dc.title.alternativeDynamic Graph Topology Learning with Non-Convex Penaltiesen_US
dc.typeChapteren_US
dc.description.versionpublishedVersionen_US
dc.rights.holderThis version will not be available due to the publisher's copyright.en_US
dc.identifier.doi10.23919/EUSIPCO55093.2022.9909609
dc.identifier.cristin2071466
cristin.ispublishedtrue
cristin.fulltextoriginal


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record