for dimensionality reduction is Principal Component Analysis (PCA) \cite{Abdi_2010}
The problem with this kind of methods is that they do not consider the explicit form of the differential manifold structure in which our data probably lie. The objective is to find a transform that goes from the original n-dimensional space to a k-dimensional space, with k << n, in which the local distances are conserved as much as possible.