Presentations

Public Thesis Defense

Marion, R. (2021). Statistical and Machine Learning Methods for Identifying Clusters of Variables: With Applications in Omics, Ecology and Psychology. Doctoral dissertation, Université catholique de Louvain.

Variable Cluster Principal Component Regression (VC-PCR)

Marion, R., Lederer, J., Govaerts, B., & von Sachs, R (2021). Robustness of Supervised Clustering Methods to Different Types of Inactive Variables. 8th Channel Network Conference, April 7-9.

Adaptive Clustering Around Latent Variables (AdaCLV)

Marion, R., Govaerts, B., & von Sachs, R. (2020). AdaCLV for interpretable variable clustering and dimensionality reduction of spectroscopic data. Chemometrics and Intelligent Laboratory Systems.

Comparison of Cluster Validity Indices (CVIs) and Decision Rules

Kaczynska, S., Marion, R., & von Sachs, R. (2020). Comparison of cluster validity indices and decision rules for different degrees of cluster separation, In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN).

Best Interpretable Rotation (BIR)

Bibal, A., Marion, R., & Frénay, B. (2018). Finding the most interpretable MDS rotation for sparse linear models based on external features, In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN).