Feature extraction by neural network nonlinear mapping for pattern classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations


Feature extraction for exploratory data projection aims for data visualization by a projection of a high-dimensional space onto two or three-dimensional space, while feature extraction for classification generally requires more than two or three features. We study extraction of more than three features, using neural network (NN) implementation of Sammon's mapping to be applied for classification. The experiments reveal that Sammon's mapping, the multilayer perceptron (MLP) and the principal component analysis (PCA) based feature extractors yield similar classification performance. We investigate a random- and PCA-based initializations of Sammon's mapping. When the PCA is applied to initialize Sammon's projection, only one experiment is required and only a fraction of the training period is needed to achieve performance comparable with that of the random initialization. Furthermore, the PCA based initialization affords better human chromosome classification performance even when using a few eigenvectors.

Original languageEnglish
Title of host publicationTrack D
Subtitle of host publicationParallel and Connectionist Systems
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Print)081867282X, 9780818672828
StatePublished - 1 Jan 1996
Event13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria
Duration: 25 Aug 199629 Aug 1996

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference13th International Conference on Pattern Recognition, ICPR 1996

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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