A Machine Learning Approach to Optimal Tikhonov Regularization I : Affine Manifolds

@inproceedings{Vito2016AML,
  title={A Machine Learning Approach to Optimal Tikhonov Regularization I : Affine Manifolds},
  author={Ernesto de Vito},
  year={2016}
}
Despite a variety of available techniques the issue of the proper regularization parameter choice for inverse problems still remains one of the biggest challenges. The main difficulty lies in constructing a rule, allowing to compute the parameter from given noisy data without relying either on a priori knowledge of the solution or on the noise level. In this paper we propose a novel method based on supervised machine learning to approximate the high-dimensional function, mapping noisy data into… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 15 references

Gamma-convergence for beginners

  • Andrea Braides
  • Lecture notes. Available on line http://www.mat…
  • 2001
Highly Influential
1 Excerpt

Little , and Mauro Maggioni . Multiresolution geometric analysis for data in high dimensions

  • Guangliang Chen, V. Anna
  • 2013

Shearlets. Multiscale analysis for multivariate data

  • Gitta Kutyniok, Demetrio Labate, editors
  • 2012

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