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

delete1998-04-01
delete40
PRE
AI
B
Boaz Lerner *
H
Hugo Guterman
M
Mayer Aladjem
D
Dinstein, I
Y
Yitzhak Romem
DOI：10.1016/S0031-3203(97)00064-2delete
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Abstract

Abstract

En 中文
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. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
chromosomes
classification
feature extraction
multilayer perceptron
neural networks
Sammon's mapping
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Journal

Pattern Recognition cover
Pattern Recognition
IF:
7.6
Papers:
1.3W
Citations:
4.5W

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