Cluster analysis is an important tool in a variety of scientific areas including pattern recognition, document clustering, and the analysis of microarray data. Although many clustering procedures such as hierarchical, strict partitioning and o

3.1 Exploring Clustering Structure in Ranking Data (Brendan Murphy)

Dec 3rd, 2014 1h 4m

Cluster analysis is concerned with finding homogeneous groups in a population. Model-based clustering methods provide a framework for developing clustering methods through the use of statistical models. This approach allows for uncertainty to

3.3 Complexity control in overlapping stochastic block models (Pierre Latouche)

Dec 3rd, 2014 54m 8s

Networks are highly used to represent complex systems as sets of interactions between units of interest. For instance, regulatory networks can describe the regulation of genes with transcriptional factors while metabolic networks focus on repr

1.2 Information Visualization: An Introduction to the Field and Applications for Statistics (Petra Isenberg)

Dec 3rd, 2014 46m 33s

Information visualization is a research area that focuses on making structures and content of large and complex data sets visually understandable and interactively analyzable. The goal of information visualization tools and techniques is to inc

4.2 A sliced inverse regression approach for block-wise evolving data streams (Jérôme Saracco)

Dec 3rd, 2014 50m 11s

In this communication, we focus on data arriving sequentially by block in a stream. A semiparametric regression model involving a common EDR (Effective Dimension Reduction) direction B is assumed in each block. Our goal is to estimate this dir

2.2 Functional estimation in high dimensional data : Application to classification (Sophie Dabo-Niang)

Dec 3rd, 2014 46m 15s

Functional data are becoming increasingly common in a variety of fields. Many studies underline the importance to consider the representation of data as functions. This has sparked a growing attention in the development of adapted statistical

4.1 Data-driven penalties: heuristics, results and thoughts... (Pascal Massart)

Dec 3rd, 2014 48m 32s

The idea of selecting a model via penalizing a log-likelihood type criterion goes back to the early seventies with the pioneering works of Mallows and Akaike. One can find many consistency results in the literature for such criteria. These res

4.3 Transfer to an Unlabeled Task using kernel marginal predictors (Gilles Blanchard)

Dec 3rd, 2014 51m 30s

We consider a classification problem: the goal is to assign class labels to an unlabeled test data set, given several labeled training data sets drawn from different but similar distributions. In essence, the goal is to predict labels from (an

2.3 Discriminative clustering for high-dimensional data (Camille Brunet)

Dec 3rd, 2014 56m 8s

A new family of 12 probabilistic models, introduced recently, aims to simultaneously cluster and visualize high-dimensional data. It is based on a mixture model which fits the data into a latent discriminative subspace with an intrinsic dimens

2.1 Hypothesis Testing and Bayesian Inference: New Applications of Kernel Methods (Arthur Gretton)

Dec 3rd, 2014 57m 28s

In the early days of kernel machines research, the "kernel trick" was considered a useful way of constructing nonlinear learning algorithms from linear ones, by applying the linear algorithms to feature space mappings of the original data. Rec

1.1 Dimension reduction based on finite mixture modeling of inverse regression (Luca Scrucca)

Dec 3rd, 2014 1h

Consider the usual regression problem in which we want to study the conditional distribution of a response Y given a set of predictors X. Sufficient dimension reduction (SDR) methods aim at replacing the high-dimensional vector of predictors

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