Unsupervised Bayesian visualization of high-dimensional data
Petri Kontkanen, Jussi Lahtinen, Petri Myllymäki, Henry Tirri · 2000
We propose a data reduction method based on a probabilistic similarity framework where tw o vectors are considered similar if they lead to similar predictions.We sho w ho w this type of a probabilistic similarity metric can be de ned 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 net w ork models.