Background: We propose a statistically principled baseline correction method, derived from a parametric smoothing model. It uses a score function to describe the key features of b...
We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to de...
We introduce a mixture of probabilistic canonical correlation analyzers model for analyzing local correlations, or more generally mutual statistical dependencies, in cooccurring d...
Many sorts of structured data are commonly stored in a multi-relational format of interrelated tables. Under this relational model, exploratory data analysis can be done by using ...
Markus Ojala, Gemma C. Garriga, Aristides Gionis, ...
Abstract. We study discriminative joint density models, that is, generative models for the joint density p(c, x) learned by maximizing a discriminative cost function, the condition...