Dimensionality reduction is commonly applied to multidimensional data to reduce the complexity of their analysis. In visual analysis systems, projections embed multidimensional data into 2D or 3D spaces for graphical representation. To facilitate a robust and accurate analysis, essential characteristics of the multidimensional data shall be preserved when projecting. Orthographic star
coordinates is a state-of-the-art linear projection method that avoids distortion of multidimensional clusters by restricting interactive exploration to orthographic projections. However, existing numerical methods for computing orthographic star coordinates have a number of limitations when putting them into practice. We overcome these limitations by proposing the novel concept of shape-preserving star coordinates where shape preservation is assured using a superset of orthographic projections. Our scheme is explicit, exact, simple, fast, parameter-free, and stable. We further present shape-preserving morphing between two shape-preserving configurations, which can be adapted for the generation of data tours.

Obs. O Prof. Molchanov estará visitando o ICMC de 7 a 18 de outubro, vai apresentar no seminário resultados do artigo "Shape Preserving Star Coordinates", apresentado no IEEE Vis 2018. https://ieeexplore.ieee.org/document/8440845