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 introduce mixture trees, a tree-based data-structure for modeling joint probability densities using a greedy hierarchical density estimation scheme. We show that the mixture tr...
In this work, a novel probability distribution is proposed to model sparse directional data. The Directional Laplacian Distribution (DLD) is a hybrid between the linear Laplacian d...
In many practical applications, the data is organized along a manifold of lower dimension than the dimension of the embedding space. This additional information can be used when le...
We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture m...