Nuisance Parameter Estimation Algorithms for Separable Nonlinear Models

Many inverse problems in machine learning, system identification, and image processing include nuisance parameters, which are important for the recovering of other parameters. Separable nonlinear optimization problems fall into this category. The special separable structure in these problems has ins...

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Veröffentlicht in:	IEEE transactions on systems, man, and cybernetics. Systems Jg. 52; H. 11; S. 7236 - 7247
Hauptverfasser:	Chen, Long, Chen, Jia-Bing, Chen, Guang-Yong, Gan, Min, Chen, C. L. Philip
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation algorithms Autoregressive processes Convergence Ear Expectation-maximization (EM) algorithm hierarchical identification algorithm Image processing Inverse problems Jacobian matrices Machine learning Mathematical models Monte Carlo methods Nuisance Optimization Parameter estimation Parameter identification Robustness (mathematics) separable nonlinear optimization problem System identification variable projection (VP) algorithm
ISSN:	2168-2216, 2168-2232
Online-Zugang:	Volltext
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