View source for Semantic mapping (statistics)

{{short description|Method in statistics}}
'''Semantic mapping''' ('''SM''') is a [[statistic|statistical]] method for [[dimensionality reduction]] (the transformation of data from a high-dimensional space into a low-dimensional space). SM can be used in a set of multidimensional vectors of features to extract a few new features that preserves the main data characteristics. 

SM performs dimensionality reduction by [[Cluster analysis|clustering]] the original features in semantic clusters and combining features mapped in the same cluster to generate an extracted feature. Given a [[data set]], this method constructs a [[projection matrix]] that can be used to map a [[data element]] from a [[high-dimensional space]] into a reduced dimensional space.

SM can be applied in construction of [[text mining]] and [[information retrieval]] systems, as well as systems managing [[Euclidean vector|vector]]s of high dimensionality. '''SM''' is an alternative to [[random mapping]], [[principal components analysis]] and [[latent semantic indexing]] methods.

==See also==
* [[Dimensionality reduction]]
* [[Principal components analysis]]
* [[Latent semantic indexing]]
* [[Unification (computer science)|Unification]] (logic reduction)

==References==

* CORRÊA, R. F.; LUDERMIR, T. B. Improving Self Organization of Document Collections by Semantic Mapping. Neurocomputing(Amsterdam), v. 70, p.&nbsp;62-69, 2006. [https://dx.doi.org/10.1016/j.neucom.2006.07.007 doi:10.1016/j.neucom.2006.07.007]
* CORRÊA, R. F. and LUDERMIR, T. B. (2007) [http://biecoll.ub.uni-bielefeld.de/volltexte/2007/133 "Dimensionality Reduction of very large document collections by Semantic Mapping"]. Proceedings of 6th Int. Workshop on Self-Organizing Maps (WSOM). {{ISBN|978-3-00-022473-7}}.

==External links==
* [http://renatocorrea.googlepages.com/ Full list of publications about Semantic Mapping method]

[[Category:Dimension reduction]]