Supervised dimensionality reduction technique accounting for soft classes

Exploratory visual analysis of multidimensional labeled data is challenging. Multidimensional Projections for labeled data attempt to separate classes while preserving neighborhoods. In this work, we consider the case where instances are assigned multiple labels with probabilities or weights: for example, the output of a probabilistic classifier, fuzzy membership functions in fuzzy logic, or the share of votes for each candidate in an election. We propose a new technique to better preserve neighborhoods of such data. Our experiments show improved qualitative results compared to unsupervised, and existing dimensionality reduction techniques.