Sentence examples for principal components such from inspiring English sources

One of DARPA's proposals is to create a "distributed tank" — a tank whose principal components, such as guns and sensors, are mounted on separate vehicles that would be controlled remotely by a soldier in yet another command vehicle.

Varimax rotation is an orthogonal transform that rotates the principal components such that the variance of the factors is maximized.

The gas-generator cycle engine was decomposed into several principal components, such as the thruster chamber, turbopumps and gas generators.

Despite the differences, all NMJs have principal components such as (1) a Schwann cell process, (2) a nerve terminal, (3) a synaptic space lined with a basement membrane, (4) a postsynaptic membrane, and (5) junctional sarcoplasm.

The idea in principal component analysis (PCA) is to transform the predictor variables into linearly independent variables or principal components—such that the first principal component has the largest variance, the second principal component has the second largest variance and is orthogonal to the first principal component, and so on.

The new coordinates are the principal components such that the first PC represents the direction of greatest variability, the second greatest variance lies on the second PC and so on.

PCA is a linear orthogonal transformation of the variables in Y to principal components in such a way that the first component has the largest possible variance; the second component has the largest possible variance of the remaining data, etc., with the total p components explaining 100% of the variance.

PCA is a data reduction technique that transforms the original variables into a set of uncorrelated variables (eigenvectors or principal components) in such a way that the first few components encompass most of the variability in the original variables.

To visualize pairwise shape differences between C57BL/6J and individual CS strain shapes, we averaged datasets containing C57BL/6J and the respective strain in MorphoJ by strain followed by PCA (principal components analysis), such that the shape change vector associated with the unique principal component represented the shape difference between the two shapes.

Coupled with the principal component analysis, such a result reinforces the course clustering according to its field of knowledge.

Methods based on Principal Component Analysis, such as Evolving Factor Analysis (EFA), are perfectly designed to obtain this information as long as the processes under study are described by full rank two-way data sets, i.e., single matrices where all process contributions are linearly independent and can be mathematically distinguished from each other.