Sammon's mapping using neural networks: A comparison

被引：49

作者：

de Ridder, D ^{[1
]}

Duin, RPW ^{[1
]}

机构：

[1] Delft Univ Technol, Fac Appl Phys, Pattern Recognit Grp, NL-2628 CJ Delft, Netherlands

来源：

PATTERN RECOGNITION LETTERS | 1997年 / 18卷 / 11-13期

关键词：

projection methods; Sammon's mapping; feed-forward neural networks; SAMANN;

D O I：

10.1016/S0167-8655(97)00093-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon's mapping. This algorithm preserves as well as possible all inter-pattern distances. A major disadvantage of the original algorithm lies in the fact that it is not easy to map hitherto unseen points. To overcome this problem, several methods have been proposed. in this paper, we aim to compare some approaches to implement this mapping on a neural network. (C) 1997 Elsevier Science B.V.

引用

页码：1307 / 1316

页数：10

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