| dc.contributor.author | Gogineni, Vinay Chakravarthi | |
| dc.contributor.author | Dasanadoddi Venkategowda, Naveen Kumar | |
| dc.contributor.author | Werner, Stefan | |
| dc.date.accessioned | 2023-02-02T12:09:01Z | |
| dc.date.available | 2023-02-02T12:09:01Z | |
| dc.date.created | 2022-11-09T18:28:18Z | |
| dc.date.issued | 2022 | |
| dc.identifier.isbn | 978-90-827970-9-1 | |
| dc.identifier.uri | https://hdl.handle.net/11250/3047994 | |
| dc.description.abstract | This 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.abstract | Dynamic Graph Topology Learning with Non-Convex Penalties | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 30th European Signal Processing Conference (EUSIPCO 2022) | |
| dc.title | Dynamic Graph Topology Learning with Non-Convex Penalties | en_US |
| dc.title.alternative | Dynamic Graph Topology Learning with Non-Convex Penalties | en_US |
| dc.type | Chapter | en_US |
| dc.description.version | publishedVersion | en_US |
| dc.rights.holder | This version will not be available due to the publisher's copyright. | en_US |
| dc.identifier.doi | 10.23919/EUSIPCO55093.2022.9909609 | |
| dc.identifier.cristin | 2071466 | |
| cristin.ispublished | true | |
| cristin.fulltext | original | |
