Sentence examples for principal component that from inspiring English sources

In particular, when considering the second principal component (that mainly accounts for the differences between samples on which linseed oil was applied and not removed, and the others), spectral regions appearing to contribute the most to the definition of the PC are very similar to those (1st component in Figure 2) identified in the case of not aged samples: (2236 2359 nm and 1868 1908 nm).

From these three measures we extracted a single principal component that described 80 percent of the variation in the three body size measures.

By filtering out the first principal component that represents expression differences between the species and/or platforms, will enable one with the remaining differences to translate mouse data to the human situation and vice versa, because species-specific variance in expression can be recognized and eliminated by subtraction.

Notably, the first principal component, that represents the greatest fraction of the variability in the gene expression, separates the prostate-derived cells from the skin-derived cells, while the second principal component, representing the second greatest amount of information in the expression data, discriminates between UGE/epidermis and UGM/dermis samples.

(B) Develop principal component that represents gene cluster C4 in the 4,767 sample data set.

AI was also correlated with three metabolite principal components, including metpc2 (P = 0.012), the principal component that is also strongly correlated with weight, triglycerides, sugar, and development time.

For every part, we compute PCA (keeping principal components that explain the 98% of data variance).

This layer consists of a few selected principal components that perform the function of hidden nodes.

For every part, we compute PCA (keeping principal components that preserve the 98% of the data variance).

Overall accuracy could be increased by optimizing the number of principal components that are used (see Fig. 9).

The dimension of the original data can be reduced by retaining only a small number of principal components that describe the predefined amount of variability.