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Representation and reconstruction of covariance operators in linear inverse problems
| Content Provider | Scilit |
|---|---|
| Author | Lila, Eardi Arridge, Simon Aston, John A. D. |
| Copyright Year | 2020 |
| Description | Journal: Inverse Problems We introduce a framework for the reconstruction and representation of functions in a setting where these objects cannot be directly observed, but only indirect and noisy measurements are available, namely an inverse problem setting. The proposed methodology can be applied either to the analysis of indirectly observed functional images or to the associated covariance operators, representing second-order information, and thus lying on a non-Euclidean space. To deal with the ill-posedness of the inverse problem, we exploit the spatial structure of the sample data by introducing a flexible regularizing term embedded in the model. Thanks to its efficiency, the proposed model is applied to MEG data, leading to a novel approach to the investigation of functional connectivity. |
| Related Links | http://arxiv.org/pdf/1806.03954 |
| ISSN | 02665611 |
| e-ISSN | 13616420 |
| DOI | 10.1088/1361-6420/ab8713 |
| Journal | Inverse Problems |
| Issue Number | 8 |
| Volume Number | 36 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2020-08-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Inverse Problems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Theoretical Computer Science Signal Processing Mathematical Physics Computer Science Applications |
