Sentence examples for multidimensional data to from inspiring English sources

Hilbert Transformation: Space-filling curves are used to map multidimensional data to one-dimensional data where they pass through every partition in a given space without any intersection with itself.

They converted multidimensional data to one methylation probability score using the inverse logit function.

Self-organizing maps are another way to reduce dimensionality by mapping multidimensional data to two dimensional space.

The aim of the diverse dimension reduction methods is, as the name implies, to reduce the essence of the interrelation of multidimensional data to a lower complexity.

The principle of this method is to reduce multidimensional data to their principal components, based on the assumption that the studied variables are not independent of each other [ 12, 13].

The PCA vector space transformation was used to reduce multidimensional data sets to lower dimensions for analysis to assess the behavior of the samples and to identify or confirm possible outliers.

Principal component analysis is an effective method to reduce multidimensional data space to its main components and, therefore improves the human perception ability of the data.

Principal component analysis (PCA) is the standard vector space transform technique used to reduce multidimensional data sets to lower dimensions for analysis.

Principal component analysis (PCA) is often used to reduce multidimensional data sets to lower dimensions for summarizing the most important part of the data while simultaneously filtering out the background errors.

Principal component analysis (PCA), a transformation method that reduces multidimensional data sets to lower dimensions, was employed to detect putative structure among 87 isolates for which complete sequence data for five loci could be obtained (Table 1).

Consequently, multidimensional data have to be folded as vector or matrix to be processed.