Data visualization with simultaneous feature selection

Dharmesh M. Maniyar; Ian T. Nabney

doi:10.1109/CIBCB.2006.330985

Data visualization with simultaneous feature selection

Dharmesh M. Maniyar, Ian T. Nabney

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

Original language	English
Title of host publication	Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06
Pages	156-163
Number of pages	8
DOIs	https://doi.org/10.1109/CIBCB.2006.330985
Publication status	Published - 2006
Event	3rd symposium on Computational Intelligence in Bioinformatics and Computational Biology - Toronto, ON, Canada Duration: 28 Sept 2006 → 29 Sept 2006

Symposium

Symposium	3rd symposium on Computational Intelligence in Bioinformatics and Computational Biology
Abbreviated title	CIBCB '06
Country/Territory	Canada
City	Toronto, ON
Period	28/09/06 → 29/09/06

Bibliographical note

© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

chemoinformatics
data mining
data visualization
feature selection
generative topographic mapping
unsupervised learning

Access to Document

10.1109/CIBCB.2006.330985

NCRG_2006_014.pdf
© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 227 KB

Cite this

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title = "Data visualization with simultaneous feature selection",

abstract = "Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. {\textcopyright} 2006 IEEE.",

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note = "{\textcopyright} 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 3rd symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '06 ; Conference date: 28-09-2006 Through 29-09-2006",

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Maniyar, DM & Nabney, IT 2006, Data visualization with simultaneous feature selection. in Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06. pp. 156-163, 3rd symposium on Computational Intelligence in Bioinformatics and Computational Biology, Toronto, ON, Canada, 28/09/06. https://doi.org/10.1109/CIBCB.2006.330985

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AU - Nabney, Ian T.

N1 - © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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N2 - Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

AB - Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

KW - chemoinformatics

KW - data mining

KW - data visualization

KW - feature selection

KW - generative topographic mapping

KW - unsupervised learning

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DO - 10.1109/CIBCB.2006.330985

M3 - Conference publication

SN - 1424406234

SN - 9781424406234

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EP - 163

BT - Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06

T2 - 3rd symposium on Computational Intelligence in Bioinformatics and Computational Biology

Y2 - 28 September 2006 through 29 September 2006

ER -

Data visualization with simultaneous feature selection

Abstract

Symposium

Bibliographical note

Keywords

Access to Document

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