Sentence examples for more principal components from inspiring English sources

The PAIRS test compares each neurons loading on the first two (or more) principal components with its "nearest neighbors" in the principal component space by computing the angle between the vectors.

This indicates that including more principal components changes descriptor behavior, while these principal components typically describe less variance than the first two components.

It can be seen from the figure that the probability of detection is improved as we include more principal components for detection; in Fig. 1, 'pc' represents the number of principal components included in test statistic.

What can be observed overall is that the use of more principal components (>3 per AA for a particular descriptor set) leads to a significant shift in the way they describe the AA differences despite being generated from the same underlying matrix.

In the companion publication of this work it was observed that using or more principal components per amino acid leads to a large shift in descriptor set behavior (characterized as the way in which a descriptor set perceives amino acid similarity) [21].

Generally, the use of more principal components (>3 per amino acid, per descriptor) leads to a significant differences in the way amino acids are described, despite that the later principal components capture less variation per component of the original input data.

Typically, the raw data matrix can be reduced to two or more principal component loadings that account for the majority of the variance.

Methods based on non-parametric multidimensional statistics (more specifically principal components analysis, PCA) were first applied to genetic data more than 30 years ago (Menozzi et al., 1978).

The projection pursuit (PP) [36] can be used since it is an iterative algorithm which can be faster than the more conventional principal component analysis (PCA) which is based on singular value decomposition.

Canonical analysis of correspondence (CAC)[ 25, 26] and more precisely principal component analysis (PCA) with respect to instrumental variables [ 27] was then used to map treatment variables and patient characteristics in a reduced space and identify (without any clustering) which patient characteristics are associated to which treatments.

More specifically, in principal components analysis we attempt to explain the total variability of the correlated variables (different types of transport) through the use of five orthogonal principal components.