This paper proposes a very general max-margin learning framework for distance-based clustering. To this end, it formulates clustering as a high order energy minimization problem w...
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hid...
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional ...
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
We propose a model for a point-referenced spatially correlated ordered categorical response and methodology for estimation of model parameters. Models and methods for spatially co...