219 citations · 1.3k across the 47 of their papers we have counts for
11 papers · 1 filter
Co-clustering based exploratory analysis of mixed-type data tables
Aichetou Bouchareb, Marc Boullé, Fabrice Clérot +1
Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as h…
Model Based Co-clustering of Mixed Numerical and Binary Data
Aichetou Bouchareb, Marc Boullé, Fabrice Clérot +1
Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix. Many approaches have been studied and have sh…
On the expressivity of bi-Lipschitz normalizing flows
Alexandre Verine, Benjamin Negrevergne, Fabrice Rossi +1
An invertible function is bi-Lipschitz if both the function and its inverse have bounded Lipschitz constants. Nowadays, most Normalizing Flows are bi-Lipschitz by design or by trai…
Graph-Based Approaches to Clustering Network-Constrained Trajectory Data
Mohamed Khalil El Mahrsi, Fabrice Rossi
Clustering trajectory data attracted considerable attention in the last few years. Most of prior work assumed that moving objects can move freely in an euclidean space and did not…
A Triclustering Approach for Time Evolving Graphs
Romain Guigourès, Marc Boullé, Fabrice Rossi
This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the sou…
Graph-Based Approaches to Clustering Network-Constrained Trajectory Data
Mohamed Khalil El Mahrsi, Fabrice Rossi
Even though clustering trajectory data attracted considerable attention in the last few years, most of prior work assumed that moving objects can move freely in an euclidean space…
Median topographic maps for biomedical data sets
Barbara Hammer, Alexander Hasenfuß, Fabrice Rossi
Median clustering extends popular neural data analysis methods such as the self-organizing map or neural gas to general data structures given by a dissimilarity matrix only. This o…
Advances in Feature Selection with Mutual Information
Michel Verleysen, Fabrice Rossi, Damien François
The selection of features that are relevant for a prediction or classification problem is an important problem in many domains involving high-dimensional data. Selecting features h…
Resampling methods for parameter-free and robust feature selection with mutual information
Damien François, Fabrice Rossi, Vincent Wertz +1
Combining the mutual information criterion with a forward feature selection strategy offers a good trade-off between optimality of the selected feature subset and computation time.…
Fast Selection of Spectral Variables with B-Spline Compression
Fabrice Rossi, Damien François, Vincent Wertz +2
The large number of spectral variables in most data sets encountered in spectral chemometrics often renders the prediction of a dependent variable uneasy. The number of variables h…
Mutual information for the selection of relevant variables in spectrometric nonlinear modelling
Fabrice Rossi, Amaury Lendasse, Damien François +2
Data from spectrophotometers form vectors of a large number of exploitable variables. Building quantitative models using these variables most often requires using a smaller set of…