Data visualization with simultaneous feature selection

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.

The role of texture amplitude in shape from shading

When a textured surface is modulated in depth and illuminated, parts of the surface receive different levels of illumination; the resulting variations in luminance can be used to infer the shape of the depth modulations-shape from shading. The changes in illumination also produce changes in the amplitude of the texture, although local contrast remains constant. We investigated the role of texture amplitude in supporting shape from shading. If a luminance plaid is added to a binary noise texture (LM), shape from shading produces perception of corrugations in two directions. If the amplitude of the noise is also modulated (AM) such that it is in-phase with one of the luminance sinusoids and out-of-phase with the other, the resulting surface is seen as corrugated in only one directionöthat supported by the in-phase pairing. We confirmed this subjective report experimentally, using a depth-mapping technique. Further, we asked naïve observers to indicate the direction of corrugations in plaids made up of various combinations of LM and AM. LM+AM was seen as having most depth, then LM-only, then LM-AM, and then AM-only. Our results suggest that while LM is required to see depth from shading, its phase relative to any AM component is also important.