Using Visual Analysis to Weight Multiple Signatures to Discriminate Complex Data

Complex data is usually represented through signatures, which are sets of features describing the data content. Several kinds of complex data allow extracting different signatures from an object, representing complementary data characteristics. However, there is no ground truth of how balancing these signatures to reach an ideal similarity distribution. It depends on the analyst intent, that is, according to the job he/she is performing, a few signatures should have more impact in the data distribution than others. This work presents a new technique, called Visual Signature Weighting (ViSW), which allows interactively analyzing the impact of each signature in the similarity of complex data represented through multiple signatures. Our method provides means to explore the tradeoff of prioritizing signatures over the others, by dynamically changing their weight relation. We also present case studies showing that the technique is useful for global dataset analysis as well as for inspecting subspaces of interest.