Principal Component Analysis
Principal Component Analysis Tool performs a principal component analysis (PCA). PCA is a useful statistical technique with application in fields such as face recognition and image compression. It is a common technique for finding patterns in data of high dimension.
Specify the cells containing the datasets in the “Input Range” entry. The entered range or ranges are grouped into the factors either by rows or by columns.
If you have labels in the first cell of each factor, select the “Labels” option.
Suppose you want to perform a principal component analysis on the data given in Figure 8-70 having the two dimensions (factors) x and y.
- Enter Sheet1!$A$1:$B$11 (or just A1:B11) in the “Input Range:” entry by typing this directly into the entry or clicking in the entry field and then selecting the range on the sheet.
- Select the “” option since the first row contains labels. (see Figure 8-69).
- Specify the output options as described above.
- Press the OK button.
The output of this principal component analysis is shown in Figure 8-75. The output shows the covariance matrix, the eigenvalues and corresponding eigenvectors. The principal component is the constructed factor with the highest percent of trace, ξ1.
