You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
| You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
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| See also: matrix multiplication |   |
Matrix multiplication is a versatile tool for many aspects of scientific
or technical methods. One particular application of matrix multiplication
is the transformation of data in n-dimensional space. Data can be scaled,
shifted, rotated, or distorted by a simple matrix multiplication. In order
to achieve all these operations by a single transformation matrix, the
original data has to be augmented by an additional constant value (preferably
1). In order to see the effects of matrix multiplication, you can start
the following 
.
Example: transformation of two-dimensional points. Suppose you have seven data points in two dimensions (x, and y). These seven data points have to be submitted to various transformation operations. Therefore we first augment the data points, denoted by [xi,yi], with a constant value, resulting in the point vectors [xi,yi,1].
For performing the various transformations, we simply have to adjust
the transformation matrix.
| Shift |  |
The coordinates of the data points are shifted by the vector [t1,t2] |
| Scaling |  |
The points are scaled by the factor s. |
| Scaling only the y coordinate |  |
Here, only the y coordinates are scaled according to the factor s. |
| Rotation |  |
A rotation of all points around the origin can be accomplished by using the sines and cosines of the rotation angle (remember the negative sign for the first sine term). |
Last Update: 2004-Jul-03