StatLearn 2012 - Workshop on "Challenging problems in Statistical Learning"

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 increase ... more

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 dimension bou... more

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 results ar... more

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 by a low... more

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. Recently, ... more

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 tools t... more

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 be quan... more

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 overlapp... more