Quality of Quantization and Visualization of Vectors Obtained by Neural Gas and Self-Organizing Map

被引：0

作者：

Kurasova, Olga ^{[1
,2
]}

Molyte, Alma ^{[1
]}

机构：

[1] Vilnius Univ, Inst Math & Informat, LT-08663 Vilnius, Lithuania

[2] Vilnius Pedag Univ, LT-08106 Vilnius, Lithuania

来源：

INFORMATICA | 2011年 / 22卷 / 01期

关键词：

self-organizing map; neural gas; multidimensional scaling; quantization error; proximity preservation; Konig's measure; Spearman's rho; DIMENSIONALITY; REDUCTION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the quality of quantization and visualization of vectors, obtained by vector quantization methods (self-organizing map and neural gas). is investigated. A multidimensional scaling is used for, visualization of multidimensional vectors. The quality of quantization is measured by a quantization error. Two numerical measures for proximity preservation (Konig's topology preservation measure and Spearman's correlation coefficient) are applied to estimate the quality of visualization. Results of visualization (mapping images) are also presented.

引用

页码：115 / 134

页数：20

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