Editing Pattern recognition (section)

===Number of important feature variables===
[[Feature selection]] algorithms attempt to directly prune out redundant or irrelevant features. A general introduction to [[feature selection]] which summarizes approaches and challenges, has been given.<ref>Isabelle Guyon Clopinet, André Elisseeff (2003). ''An Introduction to Variable and Feature Selection''. The Journal of Machine Learning Research, Vol. 3, 1157-1182. [http://www-vis.lbl.gov/~romano/mlgroup/papers/guyon03a.pdf Link] {{Webarchive|url=https://web.archive.org/web/20160304035940/http://www-vis.lbl.gov/~romano/mlgroup/papers/guyon03a.pdf |date=2016-03-04 }}</ref> The complexity of feature-selection is, because of its non-monotonous character, an [[optimization problem]] where given a total of <math>n</math> features the [[powerset]] consisting of all <math>2^n-1</math> subsets of features need to be explored. The [[Branch and bound|Branch-and-Bound algorithm]]<ref>
{{Cite journal|author1=Iman Foroutan |author2=Jack Sklansky | year=1987 |
title=Feature Selection for Automatic Classification of Non-Gaussian Data | journal=IEEE Transactions on Systems, Man, and Cybernetics | volume=17 | pages=187&ndash;198 | doi = 10.1109/TSMC.1987.4309029 | issue=2
|s2cid=9871395 }}.</ref> does reduce this complexity but is intractable for medium to large values of the number of available features <math>n</math>

Techniques to transform the raw feature vectors ('''feature extraction''') are sometimes used prior to application of the pattern-matching algorithm. [[Feature extraction]] algorithms attempt to reduce a large-dimensionality feature vector into a smaller-dimensionality vector that is easier to work with and encodes less redundancy, using mathematical techniques such as [[principal components analysis]] (PCA). The distinction between '''feature selection''' and '''feature extraction''' is that the resulting features after feature extraction has taken place are of a different sort than the original features and may not easily be interpretable, while the features left after feature selection are simply a subset of the original features.