Abstract
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.
| Original language | English |
|---|---|
| Place of Publication | Birmingham |
| Publisher | Aston University |
| Number of pages | 5 |
| ISBN (Print) | NCRG/2005/012 |
| Publication status | Published - 2 Nov 2005 |
Keywords
- generative topographic mapping
- data visualization
- simultaneous feature selection
- Expectation-Maximization algorithm
- GTM-FS