Non-Linear Principal Components Analysis using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{hinden:1997:npcaGAL,
  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",
  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}",
}

Genetic Programming entries for Hugo Hiden Mark J Willis Ming T Tham Paul Turner Gary A Montague