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- C Bernard-Michel, S Douté, +4 authors Mont
- 2008

In this paper, a statistical method is proposed to evaluate the 4 physical properties of surface materials on Mars from hyperspectral images 5 collected by the OMEGA instrument aboard the Mars express spacecraft. 6 The approach is based on the estimation of the functional relationship F be-7 tween some observed spectra and some physical parameters. To this… (More)

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

Clustering in high-dimensional spaces is a recurrent problem in many domains, for example in object recognition. High-dimensional data usually live in different low-dimensional subspaces hidden in the original space. This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts… (More)

- Laurent Amsaleg, Oussama Chelly, +4 authors Michael Nett
- KDD
- 2015

This paper is concerned with the estimation of a local measure of intrinsic dimensionality (ID) recently proposed by Houle. The local model can be regarded as an extension of Karger and Ruhl's expansion dimension to a statistical setting in which the distribution of distances to a query point is modeled in terms of a continuous random variable. This form of… (More)

Several risk measures have been proposed in the literature. In this paper, we focus on the estimation of the Conditional Tail Expectation (CTE). Its asymptotic normality has been first established in the literature under the classical assumption that the second moment of the loss variable is finite, this condition being very restrictive in practical… (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 multi-class classifier from data with uncertain labels… (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)

- Ali Gannoun, Stéphane Girard, Christiane Guinot, Jérôme Saracco
- Statistics in medicine
- 2002

Reference curves which take time into account, such as those for age, are often required in medicine, but simple systematic and efficient statistical methods for constructing them are lacking. Classical methods are based on parametric fitting (polynomial curves). Semi-parametric methods are also widely used especially in Europe. Here, we propose a new… (More)

We propose new estimates for the frontier of a set of points. They are de ned as kernel estimates covering all the points and whose associated support is of smallest surface. The estimates are written as linear combinations of kernel functions applied to the points of the sample. The coe cients of the linear combination are then computed by solving a linear… (More)

- Charles Bouveyron, Gilles Celeux, Stéphane Girard
- Pattern Recognition Letters
- 2011

A central issue in dimension reduction is choosing a sensible number of dimensions to be retained. This work demonstrates the surprising result of the asymptotic consistency of the maximum likelihood criterion for determining the intrinsic dimension of a dataset in an isotropic version of probabilistic principal component analysis (PPCA). Numerical… (More)