Sentence examples for multivariate data reduction from inspiring English sources

In this study, we employ a multivariate data reduction technique, called Factor Analysis (FA), to identify the key river health variables for a peri-urban river system, viz., the Hawkesbury-Nepean River system in New South Wales, Australia.

In combination with multivariate data reduction processes (like Factor Analysis) considered in this study, this methodology proves to be highly useful in forecasting malaria incidences in a larger spatial scale.

PCA, a well-known multivariate data reduction technique, projects the correlated higher dimensional data space into uncorrelated lower dimensional components using an orthogonal transformation.

Principal component analysis (PCA), a multivariate data reduction technique, classified all five tested powders using only three randomly selected mass spectra as a training set.

First, within each level, multivariate data reduction techniques (e.g. principal component analysis) will be applied to summarise highly correlated variables to index variables (e.g. socio economic status, Th1- related immune response).

Therefore, factor analysis is quite often used on multivariate data for dimension reduction or to condense multiple input variables to several orthogonal and meaningful factors (Hair et al. 1998).

This exploratory multivariate technique allows data reduction, i.e., it reduces the number of variables in an analysis by describing linear combinations of the variables that contain most of the information with (possibly) meaningful interpretation.

Processing these complex data can be simplified by multivariate statistical analysis, including data reduction and pattern recognition techniques, such as principal components analysis (PCA) and partial least squares discriminant analysis [15].

Principal component analysis (PCA) is a multivariate statistical technique used for data reduction and for deciphering patterns within large sets of data (Farnham et al. 2003).

Data reduction and multivariate statistical analyses.

The well-known methods for handling multivariate data are related to dimension reduction, clustering, classification, and regression.