Generalized Cross-Validation as a Method of Hyperparameter Search for MTGV Regularization
The concept of generalized cross-validation (GCV) is applied to modified total generalized variation (MTGV) regularization. Current implementations of the MTGV regularization rely on manual (or semi-manual) hyperparameter optimization, which is both time-consuming and subject to bias. The combinatio...
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| creator | Beckmann, Julian B. B Sparks, Amy Ward-Williams, Jordan A Mantle, Mick D Sederman, Andrew J Gladden, Lynn F |
| description | The concept of generalized cross-validation (GCV) is applied to modified
total generalized variation (MTGV) regularization. Current implementations of
the MTGV regularization rely on manual (or semi-manual) hyperparameter
optimization, which is both time-consuming and subject to bias. The combination
of MTGV-regularization and GCV allows for a straightforward hyperparameter
search during regularization. This significantly increases the efficiency of
the MTGV-method, because it limits the number of hyperparameters, which have to
be tested and, improves the practicality of MTGV regularization as a standard
technique for inversion of NMR signals. The combined method is applied to
simulated and experimental NMR data and the resulting reconstructed
distributions are presented. It is shown that for all data sets studied the
proposed combination of MTGV and GCV minimizes the GCV score allowing an
optimal hyperparameter choice. |
| doi_str_mv | 10.48550/arxiv.2311.11442 |
| format | Article |
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total generalized variation (MTGV) regularization. Current implementations of
the MTGV regularization rely on manual (or semi-manual) hyperparameter
optimization, which is both time-consuming and subject to bias. The combination
of MTGV-regularization and GCV allows for a straightforward hyperparameter
search during regularization. This significantly increases the efficiency of
the MTGV-method, because it limits the number of hyperparameters, which have to
be tested and, improves the practicality of MTGV regularization as a standard
technique for inversion of NMR signals. The combined method is applied to
simulated and experimental NMR data and the resulting reconstructed
distributions are presented. It is shown that for all data sets studied the
proposed combination of MTGV and GCV minimizes the GCV score allowing an
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total generalized variation (MTGV) regularization. Current implementations of
the MTGV regularization rely on manual (or semi-manual) hyperparameter
optimization, which is both time-consuming and subject to bias. The combination
of MTGV-regularization and GCV allows for a straightforward hyperparameter
search during regularization. This significantly increases the efficiency of
the MTGV-method, because it limits the number of hyperparameters, which have to
be tested and, improves the practicality of MTGV regularization as a standard
technique for inversion of NMR signals. The combined method is applied to
simulated and experimental NMR data and the resulting reconstructed
distributions are presented. It is shown that for all data sets studied the
proposed combination of MTGV and GCV minimizes the GCV score allowing an
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total generalized variation (MTGV) regularization. Current implementations of
the MTGV regularization rely on manual (or semi-manual) hyperparameter
optimization, which is both time-consuming and subject to bias. The combination
of MTGV-regularization and GCV allows for a straightforward hyperparameter
search during regularization. This significantly increases the efficiency of
the MTGV-method, because it limits the number of hyperparameters, which have to
be tested and, improves the practicality of MTGV regularization as a standard
technique for inversion of NMR signals. The combined method is applied to
simulated and experimental NMR data and the resulting reconstructed
distributions are presented. It is shown that for all data sets studied the
proposed combination of MTGV and GCV minimizes the GCV score allowing an
optimal hyperparameter choice.</abstract><doi>10.48550/arxiv.2311.11442</doi><oa>free_for_read</oa></addata></record> |
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| title | Generalized Cross-Validation as a Method of Hyperparameter Search for MTGV Regularization |
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