Unsupervised Bayesian visualization of high-dimensional data

Abstract

We propose a data reduction method based on a probabilistic similarity framework where two vectors are considered similar if they lead to similar predictions. We show how this type of a probabilistic similarity metric can be defined both in a supervised and unsupervised manner. As a concrete application of the suggested multidimensional scaling scheme, we describe how the method can be used for producing visual images of high-dimensional data, and give several examples of visualizations obtained by using the suggested scheme with probabilistic Bayesian network models.