Mapping without visualizing local default is nonsense

Sylvain Lespinats; Michael Aupetit

Mapping without visualizing local default is nonsense

Sylvain Lespinats, Michael Aupetit

Commissariat à l’énergie atomique et aux énergies alternatives

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

High-dimensional data sets are often embedded in two-dimensional spaces so as to visualize neighborhood relationships. When the map is effective (i.e. when short distances are preserved) it is a powerful way to help an analyst to understand the data set. But, mappings most often show defaults and the user is then led astray. According to this notion, a mapping should not be considered when its overall quality is not good enough. Many imperfect mappings can however be exploited by informing the user of the nature and level of defaults. In this work, we propose to visualize local indices trustworthiness and continuity for that purpose.

Original language	English
Title of host publication	Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Pages	111-116
Number of pages	6
Publication status	Published - 2010
Externally published	Yes
Event	18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 - Bruges, Belgium Duration: 28 Apr 2010 → 30 Apr 2010

Publication series

Name	Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010

Conference

Conference	18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010
Country/Territory	Belgium
City	Bruges
Period	28/04/10 → 30/04/10

Cite this

Lespinats, S., & Aupetit, M. (2010). Mapping without visualizing local default is nonsense. In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (pp. 111-116). (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).

Lespinats, Sylvain ; Aupetit, Michael. / Mapping without visualizing local default is nonsense. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. pp. 111-116 (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).

@inproceedings{b450f22a64a64bda8227e99bcb49eb4a,

title = "Mapping without visualizing local default is nonsense",

abstract = "High-dimensional data sets are often embedded in two-dimensional spaces so as to visualize neighborhood relationships. When the map is effective (i.e. when short distances are preserved) it is a powerful way to help an analyst to understand the data set. But, mappings most often show defaults and the user is then led astray. According to this notion, a mapping should not be considered when its overall quality is not good enough. Many imperfect mappings can however be exploited by informing the user of the nature and level of defaults. In this work, we propose to visualize local indices trustworthiness and continuity for that purpose.",

author = "Sylvain Lespinats and Michael Aupetit",

year = "2010",

language = "English",

isbn = "2930307102",

series = "Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010",

pages = "111--116",

booktitle = "Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010",

note = "18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 ; Conference date: 28-04-2010 Through 30-04-2010",

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Lespinats, S & Aupetit, M 2010, Mapping without visualizing local default is nonsense. in Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010, pp. 111-116, 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010, Bruges, Belgium, 28/04/10.

Mapping without visualizing local default is nonsense. / Lespinats, Sylvain; Aupetit, Michael.
Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 111-116 (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Aupetit, Michael

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N2 - High-dimensional data sets are often embedded in two-dimensional spaces so as to visualize neighborhood relationships. When the map is effective (i.e. when short distances are preserved) it is a powerful way to help an analyst to understand the data set. But, mappings most often show defaults and the user is then led astray. According to this notion, a mapping should not be considered when its overall quality is not good enough. Many imperfect mappings can however be exploited by informing the user of the nature and level of defaults. In this work, we propose to visualize local indices trustworthiness and continuity for that purpose.

AB - High-dimensional data sets are often embedded in two-dimensional spaces so as to visualize neighborhood relationships. When the map is effective (i.e. when short distances are preserved) it is a powerful way to help an analyst to understand the data set. But, mappings most often show defaults and the user is then led astray. According to this notion, a mapping should not be considered when its overall quality is not good enough. Many imperfect mappings can however be exploited by informing the user of the nature and level of defaults. In this work, we propose to visualize local indices trustworthiness and continuity for that purpose.

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T3 - Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010

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BT - Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010

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Lespinats S, Aupetit M. Mapping without visualizing local default is nonsense. In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 111-116. (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).

Mapping without visualizing local default is nonsense

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