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- Charles Bouveyron, Stéphane Girard, Cordelia Schmid
- Computational Statistics & Data Analysis
- 2007

Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that highdimensional data usually live in different low-dimensional subspaces hidden in the original space. This paper presents a family of Gaussian mixture models designed for highdimensional data… (More)

We propose a new method of discriminant analysis, called High Dimensional Discriminant Analysis (HHDA). Our approach is based on the assumption that high dimensional data live in different subspaces with low dimensionality. Thus, HDDA reduces the dimension for each class independently and regularizes class conditional covariance matrices in order to adapt… (More)

- Charles Bouveyron, Stéphane Girard
- Pattern Recognition
- 2009

In the supervised classification framework, human supervision is required for labeling a set of learning data which are then used for building the classifier. However, in many applications, human supervision is either imprecise, difficult or expensive. In this paper, the problem of learning a supervised multiclass classifier from data with uncertain labels… (More)

- Charles Bouveyron, Camille Brunet
- Computational Statistics & Data Analysis
- 2014

Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, high-dimensional data are nowadays more and more frequent and, unfortunately, classical model-based clustering techniques show a disappointing behavior in high-dimensional spaces. This is mainly due to the fact that model-based… (More)

- Charles Bouveyron, Julien Jacques
- Adv. Data Analysis and Classification
- 2011

This work develops a general procedure for clustering functional data which adapts the clustering method High Dimensional Data Clustering (HDDC), originally proposed in the multivariate context. The resulting clustering method, called funHDDC, is based on a functional latent mixture model which fits the functional data in group-specific functional… (More)

- Charles Bouveyron, Camille Brunet
- Statistics and Computing
- 2012

Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult task from both the clustering accuracy and the result understanding points of view. This paper presents a discriminative latent mixture (DLM) model which fits the data in a latent orthonormal discriminative subspace with an intrinsic… (More)

- Charles Bouveyron, Stéphane Girard, Cordelia Schmid
- SLSFS
- 2005

- Charles Bouveyron, Julien Jacques
- Pattern Recognition Letters
- 2010

The general setting of regression analysis is to identify a relationship between a response variable Y and one or several explanatory variables X by using a learning sample. In a prediction framework, the main assumption for predicting Y on a new sample of observations is that the regression model Y = f(X) + ǫ is still valid. Unfortunately, this assumption… (More)

- Charles Bouveyron, Julien Jacques
- Communications in Statistics - Simulation and…
- 2014

The present work investigates the estimation of regression mixtures when population has changed between the training and the prediction stages. Two approaches are proposed: a parametric approach modelling the relationship between dependent variables of both populations, and a Bayesian approach in which the priors on the prediction population depend on the… (More)

Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult problem. This is mainly due to the fact that high-dimensional data usually live in low-dimensional subspaces hidden in the original space. This paper presents a model-based clustering approach which models the data in a discriminative… (More)