Editing Nonlinear dimensionality reduction (section)

=== Manifold alignment ===
[[Manifold alignment]] takes advantage of the assumption that disparate data sets produced by similar generating processes will share a similar underlying manifold representation. By learning projections from each original space to the shared manifold, correspondences are recovered and knowledge from one domain can be transferred to another. Most manifold alignment techniques consider only two data sets, but the concept extends to arbitrarily many initial data sets.<ref>{{cite conference|last=Wang|first=Chang|author2=Mahadevan, Sridhar |title=Manifold Alignment using Procrustes Analysis|conference=The 25th International Conference on Machine Learning|date=July 2008|pages=1120–7|url=http://people.cs.umass.edu/~chwang/papers/ICML-2008.pdf}}</ref>