public class pca_analysis
extends java.lang.Object
Principal Components analysis is process where a set of numerical variables is broken down to another set of equal or less variables in a way that the total co-variance within the principal set is "explained" from the new set .
"PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension.It is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing data"(Lindsay I Smith ,February 26, 2002).
| Constructor and Description |
|---|
pca_analysis() |
| Modifier and Type | Method and Description |
|---|---|
void |
componize(double[][] covariate)
This is the main method of the class that perfoms the PCA analysis
|
double[] |
getsorted_Eigenvalues() |
double[][] |
getsorted_EigenVecor() |
double[] |
getsorted_Proportions() |
public void componize(double[][] covariate)
This is the main method of the class that perfoms the PCA analysis
covariate - : The prinipal set of variablespublic double[] getsorted_Eigenvalues()
public double[][] getsorted_EigenVecor()
public double[] getsorted_Proportions()