Visualizing Dissimilarity Data Using Generative Topographic Mapping
Gisbrecht A, Mokbel B, Hasenfuss A, Hammer B (2010)
In: KI 2010: Advances in Artificial Intelligence. Dillmann R, Beyerer J, Hanebeck UD, Schultz T (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 227-237.
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| Veröffentlicht | Englisch
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Autor*in
Herausgeber*in
Dillmann, Rüdiger;
Beyerer, Jürgen;
Hanebeck, Uwe D.;
Schultz, Tanja
Abstract / Bemerkung
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.0
Erscheinungsjahr
2010
Buchtitel
KI 2010: Advances in Artificial Intelligence
Serientitel
Lecture Notes in Computer Science
Seite(n)
227-237
ISBN
978-3-642-16110-0
eISBN
978-3-642-16111-7
ISSN
0302-9743
eISSN
1611-3349
Page URI
https://pub.uni-bielefeld.de/record/2982117
Zitieren
Gisbrecht A, Mokbel B, Hasenfuss A, Hammer B. Visualizing Dissimilarity Data Using Generative Topographic Mapping. In: Dillmann R, Beyerer J, Hanebeck UD, Schultz T, eds. KI 2010: Advances in Artificial Intelligence. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010: 227-237.
Gisbrecht, A., Mokbel, B., Hasenfuss, A., & Hammer, B. (2010). Visualizing Dissimilarity Data Using Generative Topographic Mapping. In R. Dillmann, J. Beyerer, U. D. Hanebeck, & T. Schultz (Eds.), Lecture Notes in Computer Science. KI 2010: Advances in Artificial Intelligence (pp. 227-237). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_26
Gisbrecht, Andrej, Mokbel, Bassam, Hasenfuss, Alexander, and Hammer, Barbara. 2010. “Visualizing Dissimilarity Data Using Generative Topographic Mapping”. In KI 2010: Advances in Artificial Intelligence, ed. Rüdiger Dillmann, Jürgen Beyerer, Uwe D. Hanebeck, and Tanja Schultz, 227-237. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg.
Gisbrecht, A., Mokbel, B., Hasenfuss, A., and Hammer, B. (2010). “Visualizing Dissimilarity Data Using Generative Topographic Mapping” in KI 2010: Advances in Artificial Intelligence, Dillmann, R., Beyerer, J., Hanebeck, U. D., and Schultz, T. eds. Lecture Notes in Computer Science (Berlin, Heidelberg: Springer Berlin Heidelberg), 227-237.
Gisbrecht, A., et al., 2010. Visualizing Dissimilarity Data Using Generative Topographic Mapping. In R. Dillmann, et al., eds. KI 2010: Advances in Artificial Intelligence. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 227-237.
A. Gisbrecht, et al., “Visualizing Dissimilarity Data Using Generative Topographic Mapping”, KI 2010: Advances in Artificial Intelligence, R. Dillmann, et al., eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp.227-237.
Gisbrecht, A., Mokbel, B., Hasenfuss, A., Hammer, B.: Visualizing Dissimilarity Data Using Generative Topographic Mapping. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., and Schultz, T. (eds.) KI 2010: Advances in Artificial Intelligence. Lecture Notes in Computer Science. p. 227-237. Springer Berlin Heidelberg, Berlin, Heidelberg (2010).
Gisbrecht, Andrej, Mokbel, Bassam, Hasenfuss, Alexander, and Hammer, Barbara. “Visualizing Dissimilarity Data Using Generative Topographic Mapping”. KI 2010: Advances in Artificial Intelligence. Ed. Rüdiger Dillmann, Jürgen Beyerer, Uwe D. Hanebeck, and Tanja Schultz. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. Lecture Notes in Computer Science. 227-237.
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