PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Data Visualization with Simultaneous Feature Selection
Dharmesh Maniyar and Ian Nabney
In: 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Sept. 28-29, 2006, Toronto, Canada.


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
ID Code:2981
Deposited By:Dharmesh Maniyar
Deposited On:12 April 2007