Principal component analysis is carried out on the data sets to determine principal components and principal component scores.

The system has three major components.

A form reference is received and used to look up principal components and principal component scores.

A number of principal components are selected.

Each fitting function is compressed through Functional Principal Component Analysis (FPCA) to generate corresponding sets of principal components.

Principal components: This method is also used in Principal Component Analysis (PCA).

Principle components: This method is also used in Principle Component Analysis (PCA).

Principal Component Analysis is used to represent the signals by a weighted combination of the principal components and these weights are telemetered to the surface.

A measuring vector (12) is produced, for each spectrum, in the low-dimensional main constituent region by the main constituent analysis of the high-dimensional spectra.

Principal component analysis is used to determine a primary component unit vector, and ambience components are determined according to a correlation-based cross-fade or an orthogonal basis derivation.

This division into components can be made using the Principal Component Analysis (PCA), the Independent Component Analysis (ICA) or the Singular Value Decomposition.

Principal components and the corresponding variables.

Rotation and Interpretation of Principal Components

The n largest principal components of the ranked transformed samples are optionally permuted to generate a re-ordered set of principal components, which are then altered by a marking angle.

In fact, even emergency notification itself is comprised of several components.