Supervised dimensionality reduction technique accounting for soft classes
Résumé
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 votes of each voters 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.
Domaines
Machine Learning [stat.ML]
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