**Multidimensional scaling** techniques are used for dimensionality reduction when the input data is not linearly arranged or it is not known whether a linear relationship exists or not. They are typically iterative and aim to minimise the difference between the distances between the pairs of points in the original input data and the distances between the corresponding pairs of points in the lower-dimensional output data. **Sammon mapping** or **Sammon projection** is the mathematical procedure that is most commonly employed to achieve this.

- alias
- MDS
- subtype
- Sammon mapping Sammon projection
- has functional building block
- FBB_Dimensionality reduction
- has input data type
- IDT_Vector of quantitative variables
- has internal model
- has output data type
- ODT_Vector of quantitative variables
- has learning style
- LST_Unsupervised
- has parametricity
- PRM_Nonparametric
- has relevance
- REL_Relevant
- uses
- sometimes supports
- ALG_Nearest Neighbour
- mathematically similar to