Adaptive matrix distances aiming at optimum regression subspaces

M. Strickert; Axel J. Soto; Gustavo E. Vazquez

Adaptive matrix distances aiming at optimum regression subspaces

M. Strickert, Axel J. Soto, Gustavo E. Vazquez

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

6 Scopus citations

Abstract

A new supervised adaptive metric approach is introduced for mapping an input vector space to a plottable low-dimensional subspace in which the pairwise distances are in maximum correlation with distances of the associated target space. The new formalism of multivariate subspace regression (MSR) is based on cost function optimization, and it allows assessing the relevance of input vector attributes. An application to molecular descriptors in a chemical compound database is presented for targeting octanol-water partitioning properties.

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	93-98
Number of pages	6
State	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

Keywords

Data-driven metric
Feature rating
Informative subspace

Cite this

Strickert, M., Soto, A. J., & Vazquez, G. E. (2010). Adaptive matrix distances aiming at optimum regression subspaces. In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (pp. 93-98). (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).

Strickert, M. ; Soto, Axel J. ; Vazquez, Gustavo E. / Adaptive matrix distances aiming at optimum regression subspaces. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. pp. 93-98 (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).

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title = "Adaptive matrix distances aiming at optimum regression subspaces",

abstract = "A new supervised adaptive metric approach is introduced for mapping an input vector space to a plottable low-dimensional subspace in which the pairwise distances are in maximum correlation with distances of the associated target space. The new formalism of multivariate subspace regression (MSR) is based on cost function optimization, and it allows assessing the relevance of input vector attributes. An application to molecular descriptors in a chemical compound database is presented for targeting octanol-water partitioning properties.",

keywords = "Data-driven metric, Feature rating, Informative subspace",

author = "M. Strickert and Soto, {Axel J.} and Vazquez, {Gustavo E.}",

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

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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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Strickert, M, Soto, AJ & Vazquez, GE 2010, Adaptive matrix distances aiming at optimum regression subspaces. 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. 93-98, 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010, Bruges, Belgium, 28/04/10.

Adaptive matrix distances aiming at optimum regression subspaces. / Strickert, M.; Soto, Axel J.; Vazquez, Gustavo E.
Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 93-98 (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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T1 - Adaptive matrix distances aiming at optimum regression subspaces

AU - Strickert, M.

AU - Soto, Axel J.

AU - Vazquez, Gustavo E.

PY - 2010

Y1 - 2010

N2 - A new supervised adaptive metric approach is introduced for mapping an input vector space to a plottable low-dimensional subspace in which the pairwise distances are in maximum correlation with distances of the associated target space. The new formalism of multivariate subspace regression (MSR) is based on cost function optimization, and it allows assessing the relevance of input vector attributes. An application to molecular descriptors in a chemical compound database is presented for targeting octanol-water partitioning properties.

AB - A new supervised adaptive metric approach is introduced for mapping an input vector space to a plottable low-dimensional subspace in which the pairwise distances are in maximum correlation with distances of the associated target space. The new formalism of multivariate subspace regression (MSR) is based on cost function optimization, and it allows assessing the relevance of input vector attributes. An application to molecular descriptors in a chemical compound database is presented for targeting octanol-water partitioning properties.

KW - Data-driven metric

KW - Feature rating

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M3 - Contribución a la conferencia

AN - SCOPUS:84886999438

SN - 2930307102

SN - 9782930307107

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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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ER -

Strickert M, Soto AJ, Vazquez GE. Adaptive matrix distances aiming at optimum regression subspaces. In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 93-98. (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).

Adaptive matrix distances aiming at optimum regression subspaces

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Keywords

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