| Zugriffsnummer | 51142 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | Joint learning of full-structure noise in hierarchical Bayesian regression models |
| Autor(in); Institution |
Hashemi, Ali; Uncertainty, Inverse Modeling and Machine Learning Group, Technische Universität Berlin, GERMANY; Institute of Software Engineering and Theoretical Computer Science, Machine Learning Group, Faculty IV Electrical Engineering and Computer Science, Technische Universität Berlin, GERMANY
Cai, Chang; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
Gao, Yijing; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
Ghosh, Sanjay; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
Müller, Klaus-Robert; Machine Learning Group, Technische Universität Berlin, GERMANY; BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, GERMANY; Department of Artificial Intelligence, Korea University, Seoul, SOUTH KOREA
Nagarajan, Srikantan S.; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
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| Quelle/Jahr | IEEE Transactions on Medical Imaging: 43 (2024), 2, 610 - 624 |
| ISSN | 0278-0062 (print) ; 1558-0062 (online) ; 1558-254X |
| DOI | |
| Persistent Identifier | |
| URL | |
| Verlag | New York, NY: IEEE |
| Freie Schlagworte | EEG/MEG Brain Source Imaging ; Hierarchical Bayesian Learning ; Majorization Minimization ; Sparse Bayesian Learning ; Type-II Maximum-Likelihood |
| Zusammenfassung | We consider the reconstruction of brain activity from electroencephalography (EEG). This inverse problem can be formulated as a linear regression with independent Gaussian scale mixture priors for both the source and noise components. Crucial factors influencing the accuracy of the source estimation are not only the noise level but also its correlation structure, but existing approaches have not addressed the estimation of noise covariance matrices with full structure. To address this shortcoming, we develop hierarchical Bayesian (type-II maximum likelihood) models for observations with latent variables for source and noise, which are estimated jointly from data. As an extension to classical sparse Bayesian learning (SBL), where across-sensor observations are assumed to be independent and identically distributed, we consider Gaussian noise with full covariance structure. Using the majorization-maximization framework and Riemannian geometry, we derive an efficient algorithm for updating the noise covariance along the manifold of positive definite matrices. We demonstrate that our algorithm has guaranteed and fast convergence and validate it in simulations and with real MEG data. Our results demonstrate that the novel framework significantly improves upon state-of-the-art techniques in the real-world scenario where the noise is indeed non-diagonal and fullstructured. Our method has applications in many domains beyond biomagnetic inverse problems. Auch unter https://www.biorxiv.org/content/10.1101/2021.11.28.470264v3 |
| Themenbereich der Metrologie | Metrologie in der Medizin |
Zitierung
Hashemi, A., Cai, C., Gao, Y., Ghosh, S., Müller, K.-R., Nagarajan, S. S., & Haufe, S. (2024). Joint learning of full-structure noise in hierarchical Bayesian regression models. IEEE Transactions on Medical Imaging, 43(2), 610–624. https://doi.org/10.1109/TMI.2022.3224085