A combined latent class and trait model for the analysis and visualization of discrete data

We present a general framework for data analysis and visualization by means of topographic organization and clustering. Imposing distributional assumptions on the assumed underlying latent factors makes the proposed model suitable for both visualization and clustering. The system noise will be modeled in parametric form, as a member of the exponential family of distributions and this allows us to deal with different (continuous or discrete) types of observables in a unified framework. In this paper, we focus on discrete case formulations which, contrary to self organizing methods for continuous data, imply variants of Bregman divergencies as measures of dissimilarity between data and reference points and, also, define the matching nonlinear relation between latent and observable variables. Therefore, the trait variant of the model can be seen as a data-driven noisy nonlinear independent component analysis, which is capable of revealing meaningful structure in the multivariate observable data and visualizing it in two dimensions. The class variant (which performs the clustering) of our model performs data-driven parametric mixture modeling. The combined (trait and class) model along with the associated estimation procedures allows us to interpret the visualization result, in the sense of a topographic ordering. One important application of this work is the discovery of underlying semantic structure in text-based documents. Experimental results on various subsets of the 20-News groups text corpus and binary coded digits data are given by way of demonstration