| has gloss | eng: Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction. Many of these non-linear dimensionality reduction methods are related to linear methods which are listed below. The non-linear methods can be broadly classified into two groups: those which actually provide a mapping (either from the high dimensional space to the low dimensional embedding or vice versa), and those that just give a visualisation. Typically those that just give a visualisation are based on proximity data - that is, distance measurements. |
