Data Visualization with Simultaneous Feature Selection

Dharmesh M. Maniyar, Ian T. Nabney
2006 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology  
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 deciding 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
more » ... eatures with a separate noise model but also gives feature saliency values which help the user assess the significance of each feature. We compare the quality of the 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.
doi:10.1109/cibcb.2006.330985 dblp:conf/cibcb/ManiyarN06 fatcat:dxknnbw52ne5dj25xl2sibapya