On pattern classification with Sammon's nonlinear mapping - An experimental study

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Abstract

Sammon's mapping is conventionally used for exploratory data projection, and as such is usually inapplicable for classification. In this paper we apply a neural network (NN) implementation of Sammon's mapping to classification by extracting an arbitrary number of projections. The projection map and classification accuracy of the mapping are compared with those of the auto-associative NN (AANN), multilayer perceptron (MLP) and principal component (PC) feature extractor for chromosome data. We demonstrate that chromosome classification based on Sammon's (unsupervised) mapping is superior to the classification based on the AANN and PC feature extractor and highly comparable with that based on the (supervised) MLP.

Original languageEnglish
Pages (from-to)371-381
Number of pages11
JournalPattern Recognition
Volume31
Issue number4
DOIs
StatePublished - 1 Jan 1998

Keywords

  • Chromosomes
  • Classification
  • Feature extraction
  • Multilayer perceptron
  • Neural networks
  • Sammon's mapping

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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