Standard multivariate dimension reduction techniques provided the definition of the landscape which we visualise in biplots and cluster trees.
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).
To address this issue, a multivariate dimension-reduction (MDR) method is proposed for PPF studies in this paper.
Here we give a brief description of the linear algebra exploited by the family of multivariate dimension-reduction methods that includes principal component analysis (PCA), log-ratio analysis (LRA), correspondence analysis (CA), and various forms of discriminant analysis.
Discriminant analysis is a multivariate dimension-reduction technique whose main goal is to extract a set of linear combinations of the quantitative variables (discriminant functions) that best reveal the differences among the groups considered [ 11].
In the multivariate analysis, the reduction in LVMI and the reduction in LA maximal and minimal volumes were independently associated with IVRT reduction.
