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.
|Title of host publication||Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06|
|Number of pages||8|
|Publication status||Published - 2006|
|Event||3rd symposium on Computational Intelligence in Bioinformatics and Computational Biology - Toronto, ON, Canada|
Duration: 28 Sep 2006 → 29 Sep 2006
|Symposium||3rd symposium on Computational Intelligence in Bioinformatics and Computational Biology|
|Abbreviated title||CIBCB '06|
|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.
- data mining
- data visualization
- feature selection
- generative topographic mapping
- unsupervised learning