Visualizing Dissimilarity Data Using Generative Topographic Mapping

被引：0

作者：

Gisbrecht, Andrej ^{[1
]}

Mokbel, Bassam ^{[1
]}

Hasenfuss, Alexander ^{[2
]}

Hammer, Barbara ^{[1
]}

机构：

[1] Univ Bielefeld, Cognit Interact Technol Ctr Excellence, Bielefeld, Germany

[2] Tech Univ Clausthal, Ctr Comp, Clausthal Zellerfeld, Germany

来源：

KI 2010: ADVANCES IN ARTIFICIAL INTELLIGENCE | 2010年 / 6359卷

关键词：

SELF-ORGANIZING MAPS; BATCH;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The generative topographic mapping (GTM) models data by a mixture of Gaussians induced by a low-dimensional lattice of latent points in low dimensional space. Using back-projection, topographic mapping and visualization can be achieved. The original GTM has been proposed for vectorial data only and, thus, cannot directly be used to visualize data given by pairwise dissimilarities only. In this contribution, we consider an extension of GTM to dissimilarity data. The method can be seen as a direct pendant to GTM if the dissimilarity matrix can be embedded in Euclidean space while constituting a model in pseudo-Euclidean space, otherwise. We compare this visualization method to recent alternative visualization tools.

引用

页码：227 / 237

页数：11

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