Sentence examples for most informative components from inspiring English sources

It is fully automatic, viz. the selection of the most informative components is based on the signal-to-noise ratio characterising processed measurement data.

To further reduce the dimensionality of the dataset, we applied Principal Component Analysis (PCA) and took the two most informative components as a quantification of the content in biographical texts.

The COA reduced the multidimensional microarray data to the three most informative components describing the dataset.

Based on the Eigenvalues, which are a measure for the amount of data variance an Eigenvector describes, the 3 most informative components of the data can be defined.

If the region of interest displays strong LD, a modest number of principal components (PCs) are generally required to capture most of the SNP variation (Supplementary Fig. S4) and   we can use a subset of the most informative components for colocalisation analysis.

Accordingly, the most informative component of the PCA was attributed to the differentiation between lineages (first axis, 10.3 % of the total variance, Fig.  2).

In contrast, disjunctive rules can be retrieved by selecting the most informative element among a subset of belief functions less informative than each of the combined ones.

The most informative principal components revealed prominent clusters consistent with the inferences gained from the phylogenetic hierarchy (i.e. closely related species were plotted nearby - results not shown).

(B ) The four most informative temporal components of the BOLD response, shown exemplary for right M1. (C ) Classification accuracy in three ROIs, using either only the first temporal component, or using the first and one additional temporal component.

SMARTPCA [ 38] was run with default settings for each of the species separately as well as for both species combined, and the three most informative principal components were selected for the plots.

We show that either avoiding variable selection and instead testing the most informative principal components or integrating over variable selection using Bayesian model averaging can help control type 1 error rates.