MétéoPrédict : prédictions de variables météorologiques à partir de mesures locales.

Session "Démo"National audienceNous présentons une interface de visualisation permettant de comparer la qualité de modèles pour la prédiction de variables météorologiques. Les modèles sont composés de réseaux de neurones à une couche cachée résultant d'apprentissages. Ils consistent à prédire le rayonnement solaire et la température extérieure sur un horizon de 24 heures à partir de mesures locales et constituent ainsi des modèles adaptés aux particularités locales comme les microclimats. Ils peuvent être comparés aux modèles naïfs classiquement utilisés pour leur simplicité de mise en œuvre tels que le modèle de la persistance. Les prédictions pourront par exemple être utilisées dans un système de régulation thermique d'un bâtiment

A low-cost variational-Bayes technique for merging mixtures of probabilistic principal component analyzers

International audienceMixtures of probabilistic principal component analyzers (MPPCA) have shown effective for modeling high-dimensional data sets living on nonlinear manifolds. Briefly stated, they conduct mixture model estimation and dimensionality reduction through a single process. This paper makes two contributions: first, we disclose a Bayesian technique for estimating such mixture models. Then, assuming several MPPCA models are available, we address the problem of aggregating them into a single MPPCA model, which should be as parsimonious as possible. We disclose in detail how this can be achieved in a cost-effective way, without sampling nor access to data, but solely requiring mixture parameters. The proposed approach is based on a novel variational-Bayes scheme operating over model parameters. Numerous experimental results and discussion are provided

Parsimonious reduction of Gaussian mixture models with a variational-Bayes approach

International audienceAggregating statistical representations of classes is an important task for current trends in scaling up learning and recognition, or for addressing them in distributed in- frastructures. In this perspective, we address the problem of merging probabilistic Gaus- sian mixture models in an efficient way, through the search for a suitable combination of components from mixtures to be merged. We propose a new Bayesian modelling of this combination problem, in association to a variational estimation technique, that handles efficiently the model complexity issue. A main feature of the present scheme is that it merely resorts to the parameters of the original mixture, ensuring low computational cost and possibly communication, should we operate on a distributed system. Experimental results are reported on real dat

Parsimonious variational-Bayes mixture aggregation with a Poisson prior

International audienceThis paper addresses merging of Gaussian mixture models, which answers growing needs in e.g. distributed pattern recognition. We propose a probabilistic model over the parameter set, that extends the weighted bipartite matching problem to our mixture aggregation task. We then derive a variational- Bayes associated estimation algorithm, that ensure low cost and parsimony, as confirmed by experimental results

Aggregation of probabilistic PCA mixtures with a variational-Bayes technique over parameters

International audienceThis paper proposes a solution to the problem of aggre- gating versatile probabilistic models, namely mixtures of probabilistic principal component analyzers. These models are a powerful generative form for capturing high-dimensional, non Gaussian, data. They simulta- neously perform mixture adjustment and dimensional- ity reduction. We demonstrate how such models may be advantageously aggregated by accessing mixture pa- rameters only, rather than original data. Aggregation is carried out through Bayesian estimation with a specific prior and an original variational scheme. Experimental results illustrate the effectiveness of the proposal

Component-level aggregation of probabilistic PCA mixtures using variational-Bayes

Technical Report. This report of an extended version of our ICPR'2010 paper.This paper proposes a technique for aggregating mixtures of probabilistic principal component analyzers, which are a powerful probabilistic generative model for coping with a high-dimensional, non linear, data set. Aggregation is carried out through Bayesian estimation with a specific prior and an original variational scheme. We demonstrate how such models may be aggregated by accessing model parameters only, rather than original data, which can be advantageous for learning from distributed data sets. Experimental results illustrate the effectiveness of the proposal

Interactive unsupervised classication and visualization for browsing an image collection

International audienceIn this paper, we propose an approach for interactive navigation in an image collection. As structured groups are more appealing to users than flat image collections, we propose an image clustering algorithm that can handle time-varying collections. A 3D graph-based visualization technique reflects the classification state. While this visualization is itself interactive, we show hos user feedback may assist the classification, thus enabling a user to improve it

Acoustic streaming measurements in annular thermoacoustic engines

Experiments with an annular thermoacoustic engine employing quasiadiabatic interaction between traveling acoustic waves and an inhomogeneously heated porous material indicate the presence of a closed-loop mass flux. A qualitative modeling of the enthalpy flux in the thermoacoustic core provides an opportunity to estimate the thermal convection associated with this mass flux, by using temperature measurement at different positions in the system. The estimated acoustically induced mass flux is in accordance with recent theoretical results

Geo-temporal structuring of a personal image database with two-level variational-Bayes mixture estimation

International audienceThis paper addresses unsupervised hierarchical classication of personal documents tagged with time and geolocation stamps. The target application is browsing among these documents. A rst partition of the data is built, based on geo-temporal measurement. The events found are then grouped according to geolocation. This is carried out through tting a two-level hierarchy of mixture models to the data. Both mixtures are estimated in a Bayesian setting, with a variational proce- dure: the classical VBEM algorithm is applied for the ner level, while a new variational-Bayes-EM algorithm is introduced to search for suitable groups of mixture components from the ner level. Experimental results are reported on articial and real data