Extension des méthodes d'analyse factorielles à des données de type intervalle

L'analyse factorielle permet d'extraire, à partir dedonnées nombreuses, les tendances les plus marquantes. à l'aidede représentations graphiques, elle visualise des groupements, des oppositions, des tendances, impossibles à discerner directement surun tableau de données. L'objectif de ce travail est d'étendre, deux méthodes en analyse factorielle, l'analyse en composantesprincipales et l'analyse des correspondances multiples à desdonnées de type intervalle. Dans la première partie de ce travail, on présente uneextension de l'analyse en composantes principales à des donnéesde type intervalle, munies éventuellement de contraintes. Onpropose deux nouvelles approches : la méthode des sommets et laméthode des centres correspondant, respectivement, à une analyseinter/intra et à une analyse inter. La visualisation ponctuelle desindividus, dans les plans factoriels, est généralisée à une visualisation de rectangles, segments ou points. On confronte dans un cadreprobabiliste la méthode des sommets et la méthode des centres. D'autre part, on établit unrapprochement entre la méthode des sommets et la méthode Statis. Dans une deuxième partie, on s'intéresse à l'analyse des correspondances multiples. On propose trois techniques de codagede variable de type intervalle : le codage croisé, le codage par sommets et le codage sansdécomposition. Les deux premières techniques se basent sur ladécomposition des variables intervalles en variables numériques.La dernière technique se base sur l'extension d'outils de codage desvariables numériques (histogramme, fonction de répartition,fonction d'appartenance etc.) à des données intervalles

Learning temporal matchings for time series discrimination

In real applications it is not rare for time series of the same class to exhibit dis- similarities in their overall behaviors, or that time series from different classes have slightly similar shapes. To discriminate between such challenging time se- ries, we present a new approach for training discriminative matching that con- nects time series with respect to the commonly shared features within classes, and the greatest differential across classes. For this, we rely on a variance/covariance criterion to strengthen or weaken matched observations according to the induced variability within and between classes. In this paper, learned discriminative matching is used to define a locally weighted time series metric, which restricts time series comparison to discriminative features. The relevance of the proposed approach is studied through a nearest neighbor time series classification on real datasets. The experiments performed demonstrate the ability of learned match- ing to capture fine-grained distinctions between time series, and outperform the standard approaches, all the more so that time series behaviors within the same class are complex

Learning Multiple Temporal Matching for Time Series Classification

12International audienceIn real applications, time series are generally of complex structure, exhibiting different global behaviors within classes. To discriminate such challenging time series, we propose a multiple temporal matching approach that reveals the commonly shared features within classes, and the most differential ones across classes. For this, we rely on a new framework based on the variance/covariance criterion to strengthen or weaken matched observations according to the induced variability within and between classes. The experiments performed on real and synthetic datasets demonstrate the ability of the multiple temporal matching approach to capture fine-grained distinctions between time series

Apprentissage d'appariements pour la discrimination de séries temporelles

Il n'est pas rare dans les applications que les profils globaux des séries temporelles soient dissimilaires au sein d'une même classe ou, inversement, exhibent des dynamiques similaires pour des classes différentes. L'objectif de ce travail consiste à discriminer de telles structures de séries temporelles complexes. Nous proposons une nouvelle approche d'apprentissage d'appariements discriminants visant à connecter les séries temporelles selon les caractéristiques partagées dans les classes et différentielles entre les classes. Cette approche est fondée sur un critère de variance/covariance pour la pénalisation des liens entre les observations en fonction de la variabilité intra et inter classes induite. Pour ce faire, l'expression de la variance/covariance classique est étendue à un ensemble de séries temporelles, puis à des classes de séries. Nous montrons ensuite comment les appariements appris peuvent être utilisés pour la définition d'une métrique locale, pondérée, restreignant la comparaison de séries à leurs attributs discriminants. Les expérimentations menées soulignent la capacité des appariements appris à révéler des signatures fines discriminantes et montrent l'efficacité de la métrique apprise pour la classification de séries temporelles complexes.It is not rare in applications for global profiles of time series to be different within a class, or, on the contrary, to show similar profiles for different classes. The aim of this work is to discriminate such complex structures of time series. We propose a new approach to learn discriminative matchings, in order to connect a set of time series, according to the common features within the classes as well as the differenciating features between the classes. Our approach is based on variance-covariance criteria, for the penalisation of links between observations, due to the variability induced within and between classes . For this, the classical expression for the variance/covariance is extended to a set of time series, then to a partition of those series. We show then how the learned matchings can be used to define a weighted local metric, restricting the comparison of the series to their discriminative features. Experiments have been conducted that underline the ability of the learned matchings to reveal accurate discriminative signatures and show the effectiveness of the learned metric to classify complex time series.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Prédictions d'activité dans les réseaux sociaux en ligne

National audienceOnline Platforms dedicated to social networking host new social phenomenons. Thus several keywords may suddenly take an unprecedented importance, reflecting the number of dis- cussions they have raised within a short time period. Such bursts in topic discussions are usually referred to as buzz events. We address in this paper the problem of predicting the activity volume associated to a given keyword without a priori knowledge on the underlying social network. To do so, we propose to define social netowrk on a content-centric way. Our approach is evaluated at "industrial scale" on two different social networks: Twitter, a platform with extremely fast dynamics (Kwak et al., 2010), and Tom's Hardware, a worldwide forum network focusing on new technology. The experiments conducted reveal that it is possible to predict activity volume associated to a keyword in social media with high accuracy

Multiple Metric Learning for large margin kNN Classification of time series

International audienceTime series are complex data objects, they may present noise, varying delays or involve several temporal granularities. To classify time series, promising solutions refer to the combination of multiple basic metrics to compare time series according to several characteristics. This work proposes a new framework to learn a combination of multiple metrics for a robust kNN classifier. By introducing the concept of pairwise space, the combination function is learned in this new space through a "large margin" optimization process. We apply it to compare time series on both their values and behaviors. The efficiency of the learned metric is compared to the major alternative metrics on large public datasets

Querying Temporal Drifts at Multiple Granularities

There exists a large body of work on online drift detection with the goal of dynamically finding and maintaining changes in data streams. In this paper, we adopt a query-based approach to drift detection. Our approach relies on a drift index, a structure that captures drift at different time granularities and enables flexible drift queries. We formalize different drift queries that represent real-world scenarios and develop query evaluation algorithms that use different mate-rializations of the drift index as well as strategies for online index maintenance. We describe a thorough study of the performance of our algorithms on real-world and synthetic datasets with varying change rates

Querying Temporal Drifts at Multiple Granularities

There exists a large body of work on online drift detection with the goal of dynamically finding and maintaining changes in data streams. In this paper, we adopt a query-based approach to drift detection. Our approach relies on a drift index, a structure that captures drift at different time granularities and enables flexible drift queries. We formalize different drift queries that represent real-world scenarios and develop query evaluation algorithms that use different mate-rializations of the drift index as well as strategies for online index maintenance. We describe a thorough study of the performance of our algorithms on real-world and synthetic datasets with varying change rates

Réduction de la dimension temporelle de séries multivariées par extraction des tendances locales

L'application des méthodes d'analyse de données à des séries temporelles se trouve vite limitée par le nombre souvent très élevé des observations composant les séries. Ce travail propose une nouvelle technique de réduction de la dimension temporelle d'une série multivariée préservant la structure de variance-covariance, élément de base pour de nombreuses méthodes d'analyse de données. Cette nouvelle technique est fondée sur l'extraction des principales tendances locales composant la série et permet la conservation de la dimension temporelle des séries réduites. Les résultats de cette approche sont illustrés sur une application médicale en anesthésie-réanimation