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2020 – today
- 2024
[j4]Sacha Corbugy, Rebecca Marion, Benoît Frénay:
Gradient-based explanation for non-linear non-parametric dimensionality reduction. Data Min. Knowl. Discov. 38(6): 3690-3718 (2024)- [j3]Rebecca Marion
, Benoît Frénay:
Improving the Feature Selection Stability of the Delta Test in Regression. IEEE Trans. Artif. Intell. 5(5): 1911-1917 (2024) - 2022
- [c3]Pierre Lambert, Rebecca Marion, Julien Albert, Emmanuel Jean, Sacha Corbugy, Cyril de Bodt:
Globally local and fast explanations of t-SNE-like nonlinear embeddings. CIKM Workshops 2022 - [i1]Rebecca Marion, Johannes Lederer, Bernadette Govaerts, Rainer von Sachs:
VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering. CoRR abs/2202.00975 (2022) - 2021
- [b1]Rebecca Marion:
Statistical and machine learning methods for identifying clusters of variables: with applications in omics, ecology and psychology. Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2021 - [j2]Adrien Bibal, Rebecca Marion, Rainer von Sachs, Benoît Frénay:
BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation. Neurocomputing 453: 109-118 (2021) - 2020
- [c2]Sara Kaczynska, Rebecca Marion, Rainer von Sachs:
Comparison of Cluster Validity Indices and Decision Rules for Different Degrees of Cluster Separation. ESANN 2020: 369-374
2010 – 2019
- 2019
- [j1]Rebecca Marion, Adrien Bibal, Benoît Frénay:
BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables. Neurocomputing 342: 83-96 (2019) - 2018
- [c1]Adrien Bibal, Rebecca Marion, Benoît Frénay:
Finding the most interpretable MDS rotation for sparse linear models based on external features. ESANN 2018
Coauthor Index
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