Jérôme Lapuyade-Lahorgue

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Hidden Markov chains (HMC) are a very powerful tool in hidden data restoration and are currently used to solve a wide range of problems. However, when these data are not stationary, estimating the parameters, which are required for unsupervised processing, poses a problem. Moreover, taking into account correlated non-Gaussian noise is difficult without(More)
The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable noisy version. Classically, the distribution p(y|x) is simple enough to ensure the Markovianity of p(x|y), that enables one to use different Bayesian restoration techniques. HMC model has recently been extended to(More)
In the classical hidden Markov chain (HMC) model we have a hidden chain X , which is a Markov one and an observed chain Y. HMC are widely used; however, in some situations they have to be replaced by the more general " hidden semi-Markov chains " (HSMC), which are particular " triplet Markov chains " (TMC)) , , (Y U X T = , where the auxiliary chain U(More)
Parametric modeling and estimation of non-Gaussian multidimensional probability density function is a difficult problem whose solution is required by many applications in signal and image processing. A lot of efforts have been devoted to escape the usual Gaussian assumption by developing perturbed Gaussian models such as Spherically Invariant Random Vectors(More)
Keywords: Hidden semi-Markov chains Unsupervised signal segmentation Long dependence noise Iterative conditional estimation Unsupervised signal segmentation Unsupervised image segmentation a b s t r a c t The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable ''noisy(More)
Determining the number of components in dimensionality reduction techniques is still one of the open problems of research on data analysis. These methods are often used in knowledge extraction of mul-tivariate great dimensional data, but very often the number of components is assumed to be known. One of the classical methods to estimate this dimensionality(More)
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