Visualizing distortions in continuous projection techniques.

Michael Aupetit

Visualizing distortions in continuous projection techniques.

Michael Aupetit^*

^*Corresponding author for this work

Commissariat à l’énergie atomique et aux énergies alternatives

Research output: Contribution to conference › Paper › peer-review

Abstract

The visualization of multi-dimensional data has been studied for a long time. Here we propose to visualize any distortion measure associated to a projected datum in continuous projection techniques, by coloring its corresponding Vorono'i cell in the projection space. We apply this approach to detect where the high-dimensional manifold has been torn or glued during the projection. We experiment this technique with the Principal Component Analysis and the Curvilinear Component Analysis for different databases.

Original language	English
Pages	465-470
Number of pages	6
Publication status	Published - 2004
Externally published	Yes
Event	12th European Symposium on Artificial Neural Networks, ESANN 2004 - Bruges, Belgium Duration: 28 Apr 2004 → 30 Apr 2004

Conference

Conference	12th European Symposium on Artificial Neural Networks, ESANN 2004
Country/Territory	Belgium
City	Bruges
Period	28/04/04 → 30/04/04

Cite this

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title = "Visualizing distortions in continuous projection techniques.",

abstract = "The visualization of multi-dimensional data has been studied for a long time. Here we propose to visualize any distortion measure associated to a projected datum in continuous projection techniques, by coloring its corresponding Vorono'i cell in the projection space. We apply this approach to detect where the high-dimensional manifold has been torn or glued during the projection. We experiment this technique with the Principal Component Analysis and the Curvilinear Component Analysis for different databases.",

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AU - Aupetit, Michael

PY - 2004

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AB - The visualization of multi-dimensional data has been studied for a long time. Here we propose to visualize any distortion measure associated to a projected datum in continuous projection techniques, by coloring its corresponding Vorono'i cell in the projection space. We apply this approach to detect where the high-dimensional manifold has been torn or glued during the projection. We experiment this technique with the Principal Component Analysis and the Curvilinear Component Analysis for different databases.

UR - https://www.scopus.com/pages/publications/105034885176

M3 - Paper

AN - SCOPUS:105034885176

SP - 465

EP - 470

T2 - 12th European Symposium on Artificial Neural Networks, ESANN 2004

Y2 - 28 April 2004 through 30 April 2004

ER -

Visualizing distortions in continuous projection techniques.

Abstract

Conference

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