University logo Department logo
  • Home
  • Research Topics
  • Projects
  • Open Positions
  • Members
  • Publications
  • Contact

Rebecca Marion

Postdoctoral Researcher

Interests

  • stability
  • feature selection
  • dimensionality reduction
  • feature clustering
  • sparsity

Education

  • PhD in Statistics, 2021

    Université catholique de Louvain

  • M.Sc. in Statistics, 2016

    Université catholique de Louvain

Projects

  • ARIAC

Biography

Rebecca (Becca) is a Postdoctoral Researcher developing methods for improving the stability of feature selection and dimensionality reduction techniques. She completed a PhD in Statistics at UCLouvain (Belgium) in 2021, and her thesis covered the subjects of clustering, dimensionality reduction and feature selection for data with grouped features.

Publications

BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation

Adrien Bibal , Rebecca Marion , Rainer von Sachs , Benoît Frénay
Neurocomputing
September, 2021
Details PDF

BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables

Rebecca Marion , Adrien Bibal , Benoît Frénay
Neurocomputing
May, 2019
Details PDF Code

Finding the most interpretable MDS rotation for sparse linear models based on external features.

Adrien Bibal , Rebecca Marion , Benoît Frénay
ESANN
January, 2018
Details PDF

This website was created by HuMaLearn Team ©