Editing Nonlinear dimensionality reduction (section)

== Methods based on proximity matrices ==
A method based on proximity matrices is one where the data is presented to the algorithm in the form of a [[similarity matrix]] or a [[distance matrix]]. These methods all fall under the broader class of [[Multidimensional scaling#Types|metric multidimensional scaling]]. The variations tend to be differences in how the proximity data is computed; for example, [[isomap]], [[locally linear embeddings]], [[maximum variance unfolding]], and [[Sammon's projection|Sammon mapping]] (which is not in fact a mapping) are examples of metric multidimensional scaling methods.