We propose a generative topographic mapping (GTM) based data visualization with simultaneous feature selection (GTM-FS) approach which not only provides a better visualization by modeling irrelevant features ("noise") using a separate shared distribution but also gives a saliency value for each feature which helps the user to assess their significance. This technical report presents a varient of the Expectation-Maximization (EM) algorithm for GTM-FS.
|Place of Publication||Birmingham|
|Number of pages||5|
|Publication status||Published - 2 Nov 2005|
- generative topographic mapping
- data visualization
- simultaneous feature selection
- Expectation-Maximization algorithm