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Communication Dans Un Congrès Année : 2022

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.
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Dates et versions

cea-04176573 , version 1 (03-08-2023)

Identifiants

Citer

Sorina Mustatea, Michaël Aupetit, Jaakko Peltonen, Sylvain Lespinats, Denys Dutykh. Supervised dimensionality reduction technique accounting for soft classes. ESANN 2022 : European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Oct 2022, Bruges, Belgium. pp.13, ⟨10.14428/esann/2022.ES2022-26⟩. ⟨cea-04176573⟩
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