Probabilistic Enhanced Mapping with the Generative Tabular Model

Abstract

Visualization of the massive datasets needs new methods which are able to quickly and easily reveal their contents. The projection of the data cloud is an interesting paradigm in spite of its difficulty to be explored when data plots are too numerous. So we study a new way to show a bidimensional projection from a multidimensional data cloud: our generative model constructs a tabular view of the projected cloud. We are able to show the high densities areas by their non equidistributed discretization. This approach is an alternative to the self-organizing map when a projection does already exist. The resulting pixel views of a dataset are illustrated by projecting a data sample of real images: it becomes possible to observe how are laid out the class labels or the frequencies of a group of modalities without being lost because of a zoom enlarging change for instance. The conclusion gives perspectives to this original promising point of view to get a readable projection for a statistical data analysis of large data samples.