209 citations · 274 across the 6 of their papers we have counts for
9 papers
Interpretable sparse SIR for functional data
Victor Picheny, Rémi Servien, Nathalie Villa-Vialaneix
This work focuses on the issue of variable selection in functional regression. Unlike most work in this framework, our approach does not select isolated points in the definition do…
Random Forests for Big Data
Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot +1
Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but t…
On-line relational SOM for dissimilarity data
Madalina Olteanu, Nathalie Villa-Vialaneix, Marie Cottrell
In some applications and in order to address real world situations better, data may be more complex than simple vectors. In some examples, they can be known through their pairwise…
Neural Networks for Complex Data
Marie Cottrell, Madalina Olteanu, Fabrice Rossi +2
Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved…
Consistency of functional learning methods based on derivatives
Fabrice Rossi, Nathalie Villa-Vialaneix
In some real world applications, such as spectrometry, functional models achieve better predictive performances if they work on the derivatives of order m of their inputs rather th…
Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach
Fabrice Rossi, Nathalie Villa-Vialaneix
This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produce…