Procrustes Analysis
The program performs a least-squares orthogonal generalized Procrustes analysis (least-squares orthogonal mapping). Procrustes analysis is a method of comparing two sets of data. The method is based on matching corresponding points (landmarks) from each of the two data sets.
Landmarks are points that accurately describe a shape. Corresponding landmarks would be the same landmark on two different shapes.
Procrustes analysis is a rigid shape analysis that uses isomorphic scaling, translation, and rotation to find the “best” fit between two or more landmarked shapes.
FindGraph uses scaling, translation, rotation, and additionally stretching/compressing and shearing transformations. It applies nonlinear mapping algorithm to find best fit, i.e. to find a reference cluster of landmarks, so that the distance of each reference landmark to it's corresponding 'experimental' landmark is minimised. Data points can be given greater or less influence over the Procrustes analysis by assigning a weight to each point. We get weight as parameter Z in data series selected in 'Reference claster' combobox.
To you define a reference cluster of landmarks you have to select the data series and to define N marks.
There are different ways to define an 'experimental' cluster of landmarks:
- select the points by hand;
- take first N points in series;
- find best pattern of N points in series.
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