Transformation of measurement uncertainties into low-dimensional feature vector space
Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2021-03-01
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| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.201086 |
| _version_ | 1818339121895047168 |
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| author | A. Alexiadis S. Ferson E. A. Patterson |
| author_facet | A. Alexiadis S. Ferson E. A. Patterson |
| author_sort | A. Alexiadis |
| collection | DOAJ |
| description | Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for postprocessing or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples, Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose the global ocean temperature data. The uses of the method range from supporting decision-making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterization of the El Niño Southern Oscillation. |
| first_indexed | 2024-12-13T15:21:59Z |
| format | Article |
| id | doaj.art-c67c22848f7c4208ac2d7ce4acc6bf45 |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-13T15:21:59Z |
| publishDate | 2021-03-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-c67c22848f7c4208ac2d7ce4acc6bf452022-12-21T23:40:33ZengThe Royal SocietyRoyal Society Open Science2054-57032021-03-018310.1098/rsos.201086201086Transformation of measurement uncertainties into low-dimensional feature vector spaceA. AlexiadisS. FersonE. A. PattersonAdvances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for postprocessing or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples, Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose the global ocean temperature data. The uses of the method range from supporting decision-making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterization of the El Niño Southern Oscillation.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.201086measurement uncertaintydata decompositionapproximate bayesian computationsoil moistureensostrain analysis |
| spellingShingle | A. Alexiadis S. Ferson E. A. Patterson Transformation of measurement uncertainties into low-dimensional feature vector space Royal Society Open Science measurement uncertainty data decomposition approximate bayesian computation soil moisture enso strain analysis |
| title | Transformation of measurement uncertainties into low-dimensional feature vector space |
| title_full | Transformation of measurement uncertainties into low-dimensional feature vector space |
| title_fullStr | Transformation of measurement uncertainties into low-dimensional feature vector space |
| title_full_unstemmed | Transformation of measurement uncertainties into low-dimensional feature vector space |
| title_short | Transformation of measurement uncertainties into low-dimensional feature vector space |
| title_sort | transformation of measurement uncertainties into low dimensional feature vector space |
| topic | measurement uncertainty data decomposition approximate bayesian computation soil moisture enso strain analysis |
| url | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.201086 |
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