The goal of sufficient dimension reduction in supervised learning is to find the lowdimensional subspace of input features that is `sufficient' for predicting output values. ...
In this paper we shall derive a posteriori error estimates in the L1-norm for upwind finite volume schemes for the discretization of nonlinear conservation laws on unstructured gri...
Video and image datasets can often be described by a small number of parameters, even though each image usually consists of hundreds or thousands of pixels. This observation is of...
In arbitrary dimension, we consider the semi-discrete elliptic operator -2 t + AM , where AM is a finite difference approximation of the operator - x((x) x). For this operator we d...
Abstract—Based on information theoretic tools, a new spectrum sensing method is proposed in this paper to detect vacant sub-bands in the radio spectrum1 . Specifically, based on...