Traditionally, the Universal Background Model (UBM) is viewed as the background model of the entire acoustic feature space. We propose a novel interpretation of the UBM model, and...
For a finite set of points lying on a lower dimensional manifold embedded in a high-dimensional data space, algorithms have been developed to study the manifold structure. Howeve...
We present a new approach to the general problem of template-based segmentation, detection, and registration. This joint problem is highly nonlinear and high dimensional, due to t...
We propose a novel, computationally efficient generative topographic model for inferring low dimensional representations of high dimensional data sets, designed to exploit data s...
We describe a way of using multiple different types of similarity relationship to learn a low-dimensional embedding of a dataset. Our method chooses different, possibly overlappin...