- author = "Hugo Hiden and Mark Willis and Ming Tham and Paul Turner and Gary Montague",
- title = "Non-Linear Principal Components Analysis using Genetic Programming",
- booktitle = "Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA",
- year = "1997",
- editor = "Ali Zalzala",
- pages = "302--307",
- address = "University of Strathclyde, Glasgow, UK",
- publisher_address = "Savoy Place, London WC2R 0BL, UK",
- month = "1-4 " # sep,
- publisher = "Institution of Electrical Engineers",
- keywords = "genetic algorithms, genetic programming, data analysis, multivariate statistics",
- ISBN = "0-85296-693-8",
- broken = "http://lorien.ncl.ac.uk/sorg/paper13.ps",
-
URL = "
http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019970CP446000302000001&idtype=cvips&prog=normal",
-
DOI = "
doi:10.1049/cp:19971197",
- size = "6 pages",
- abstract = "Principal Components Analysis (PCA) is a standard statistical technique, which is frequently employed in the analysis of large highly correlated data-sets. As it stands, PCA is a linear technique which can limit its relevance to the highly non-linear systems frequently encountered in the chemical process industries. Several attempts to extend linear PCA to cover non-linear data sets have been made, and will be briefly reviewed in this paper. We propose a symbolically oriented technique for non-linear PCA, which is based on the Genetic Programming (GP) paradigm. Its applicability will be demonstrated using two simple non-linear systems and industrial data collected from a distillation column. It is suggested that the use of the GP based non-linear PCA algorithm achieves the objectives of non-linear PCA, while giving high a degree of structural parsimony.",
-
notes = "GALESIA'97
see also \cite{hiden:1999:CCE}",
