Editing Granular computing (section)

====Variable transformation====
A number of classical methods, such as [[principal component analysis]], [[multidimensional scaling]], [[factor analysis]], and [[structural equation modeling]], and their relatives, fall under the genus of "variable transformation."  Also in this category are more modern areas of study such as [[dimensionality reduction]], [[projection pursuit]], and [[independent component analysis]].  The common goal of these methods in general is to find a representation of the data in terms of new variables, which are a linear or nonlinear transformation of the original variables, and in which important statistical relationships emerge. The resulting variable sets are almost always smaller than the original variable set, and hence these methods can be loosely said to impose a granulation on the feature space.  These dimensionality reduction methods are all reviewed in the standard texts, such as {{Harvtxt|Duda|Hart|Stork|2001}}, {{Harvtxt|Witten|Frank|2005}}, and {{Harvtxt|Hastie|Tibshirani|Friedman|2001}}.