In the following I want to show how the afore said works in detail applied to a tetrahedron.
Figure A.1 shows an arbitrary constellation of four points which form a tetrahedron. In the following a step by step recipe is given for applying PCA to this constellation.
First three sets are formed, namely , , and , by collecting the coordinates of the four tetrahedron points :
and | (A.12) |
and | (A.13) |
For PCA the next step is to subtract the mean from each data set, so three new sets are defined:
Note that the mean value of the three data sets , , and presented in Equation (A.14) is equal to zero, i.e. .
With respect to Equation (A.8) a covariance matrix is formed from the three mean adjusted data sets , , and , see Equation (A.14).
Under the assumption that Equation (A.11) holds, there exist exact three real eigenvalues , , and (since the covariance matrix is symmetric) and three corresponding orthogonal eigenvectors , , and . If the three eigenvalues are not different, the corresponding eigenvectors cannot be determined uniquely. In this case an arbitrary system of three orthogonal vectors is used for a so-called ellipsoidal glyph visualization.