Neurocomputing, 150(B), p. 611–626 (2015);doi:10,1016/j.neucom.2014.09.061
Projecting multi-dimensional data to a lower-dimensional visual display is a commonly used approach for identifying and analyzing patterns in data. Many dimensionality reduction techniques exist for generating visual embeddings, but it is often hard to avoid cluttered projections when the data is large in size and noisy. For many application users who are not machine learning experts, it is difficult to control the process in order to improve the “readability” of the projection and at the same time to understand their quality. In this paper, we propose a simple interactive feature transformation approach that allows the analyst to de-clutter the visualization by gradually transforming the original feature space based on existing class knowledge. By changing a single parameter, the user can easily decide the desired trade-off between structural preservation and the visual quality during the transforming process. The proposed approach integrates semi-interactive feature transformation techniques as well as a variety of quality measures to help analysts generate uncluttered projections and understand their quality.