Recent advances in nonlinear dimensionality reduction, manifold and topological learning

Axel Wismüller; Michel Verleysen; Michael Aupetit; John A. Lee

Recent advances in nonlinear dimensionality reduction, manifold and topological learning

Axel Wismüller, Michel Verleysen, Michael Aupetit, John A. Lee

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

Abstract

The ever-growing amount of data stored in digital databases raises the question of how to organize and extract useful knowledge. This paper outlines some current developments in the domains of dimensionality reduction, manifold learning, and topological learning. Several aspects are dealt with, ranging from novel algorithmic approaches to their realworld applications. The issue of quality assessment is also considered and progress in quantitive as well as visual crieria is reported.

Original language	English
Title of host publication	Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Publisher	i6doc.com publication
Pages	71-80
Number of pages	10
ISBN (Print)	2930307102, 9782930307107
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

Wismüller, A., Verleysen, M., Aupetit, M., & Lee, J. A. (2010). Recent advances in nonlinear dimensionality reduction, manifold and topological learning. In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (pp. 71-80). (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010). i6doc.com publication.

Wismüller, Axel ; Verleysen, Michel ; Aupetit, Michael et al. / Recent advances in nonlinear dimensionality reduction, manifold and topological learning. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. i6doc.com publication, 2010. pp. 71-80 (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).

@inproceedings{7bd8717c5b994418ac0876507451069d,

title = "Recent advances in nonlinear dimensionality reduction, manifold and topological learning",

abstract = "The ever-growing amount of data stored in digital databases raises the question of how to organize and extract useful knowledge. This paper outlines some current developments in the domains of dimensionality reduction, manifold learning, and topological learning. Several aspects are dealt with, ranging from novel algorithmic approaches to their realworld applications. The issue of quality assessment is also considered and progress in quantitive as well as visual crieria is reported.",

author = "Axel Wism{\"u}ller and Michel Verleysen and Michael Aupetit and Lee, \{John A.\}",

year = "2010",

language = "English",

isbn = "2930307102",

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

publisher = "i6doc.com publication",

pages = "71--80",

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",

}

Wismüller, A, Verleysen, M, Aupetit, M & Lee, JA 2010, Recent advances in nonlinear dimensionality reduction, manifold and topological learning. 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, i6doc.com publication, pp. 71-80, 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010, Bruges, Belgium, 28/04/10.

Recent advances in nonlinear dimensionality reduction, manifold and topological learning. / Wismüller, Axel; Verleysen, Michel; Aupetit, Michael et al.
Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. i6doc.com publication, 2010. p. 71-80 (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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M3 - Conference contribution

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SN - 2930307102

SN - 9782930307107

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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Wismüller A, Verleysen M, Aupetit M, Lee JA. Recent advances in nonlinear dimensionality reduction, manifold and topological learning. In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. i6doc.com publication. 2010. p. 71-80. (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).

Recent advances in nonlinear dimensionality reduction, manifold and topological learning

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