Transformation of measurement uncertainties into low-dimensional feature vector space.

ABSTRACT: 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 statistica

SUBMITTER: Alexiadis A 

PROVIDER: S-EPMC8074961 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature