Data visualization with simultaneous feature selection


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.

Publication DOI:
Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Aston University (General)
Additional Information: © 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.
Event Title: 3rd symposium on Computational Intelligence in Bioinformatics and Computational Biology
Event Type: Other
Event Dates: 2006-09-28 - 2006-09-29
Uncontrolled Keywords: chemoinformatics,data mining,data visualization,feature selection,generative topographic mapping,unsupervised learning,Artificial Intelligence,Biomedical Engineering,Applied Mathematics,Computational Mathematics
ISBN: 1424406234, 9781424406234
Last Modified: 15 Jul 2024 08:38
Date Deposited: 10 Sep 2009 14:23
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2006
Authors: Maniyar, Dharmesh M.
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)



Version: Accepted Version

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