Types
MDS algorithms fall into a taxonomy, depending on the meaning of the input matrix:
- Classical multidimensional scaling
- Also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson–Gower scaling. Takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.
- Metric multidimensional scaling
- A superset of classical MDS that generalizes the optimization procedure to a variety of loss functions and input matrices of known distances with weights and so on. A useful loss function in this context is called stress, which is often minimized using a procedure called stress majorization.
- Non-metric multidimensional scaling
- In contrast to metric MDS, non-metric MDS finds both a non-parametric monotonic relationship between the dissimilarities in the item-item matrix and the Euclidean distances between items, and the location of each item in the low-dimensional space. The relationship is typically found using isotonic regression.
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- Louis Guttman's smallest space analysis (SSA) is an example of a non-metric MDS procedure.
- Generalized multidimensional scaling
- An extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In case when the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.
Read more about this topic: Multidimensional Scaling
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