Finding the most interpretable MDS rotation for sparse linear models based on external features. - Adrien Bibal; Rebecca Marion; Benoît Frénay (ESANN, 2018)

BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables. - Rebecca Marion; Adrien Bibal; Benoît Frénay (Neurocomputing, 2019)

Comparison of Cluster Validity Indices and Decision Rules for Different Degrees of Cluster Separation. - Sara Kaczynska; Rebecca Marion; Rainer von Sachs (ESANN, 2020)

BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation. - Adrien Bibal; Rebecca Marion; Rainer von Sachs; Benoît Frénay (Neurocomputing, 2021)

Statistical and machine learning methods for identifying clusters of variables: with applications in omics, ecology and psychology. - Rebecca Marion (2021)

Globally local and fast explanations of t-SNE-like nonlinear embeddings. - Pierre Lambert; Rebecca Marion; Julien Albert; Emmanuel Jean; Sacha Corbugy; Cyril de Bodt (CIKM Workshops, 2022)

VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering. - Rebecca Marion; Johannes Lederer; Bernadette Govaerts; Rainer von Sachs (CoRR, 2022)

Improving the Feature Selection Stability of the Delta Test in Regression. - Rebecca Marion; Benoît Frénay (IEEE Trans. Artif. Intell., 2024)

Gradient-based explanation for non-linear non-parametric dimensionality reduction. - Sacha Corbugy; Rebecca Marion; Benoît Frénay (Data Min. Knowl. Discov., 2024)