To be efficient, data protection algorithms should generally exploit the properties of the media information in the transform domain. In this paper, we will advocate the use of no...
Philippe Jost, Pierre Vandergheynst, Pascal Frossa...
In this paper we show that iterative rounding is a powerful and flexible tool in the design of approximation algorithms for multiobjective optimization problems. We illustrate tha...
— We consider dual subgradient methods for solving (nonsmooth) convex constrained optimization problems. Our focus is on generating approximate primal solutions with performance ...
We demonstrate that an algorithm proposed by Drineas et. al. in [7] to approximate the singular vectors/values of a matrix A, is not only of theoretical interest but also a fast, v...
In recent years, metric learning in the semisupervised setting has aroused a lot of research interests. One type of semi-supervised metric learning utilizes supervisory informatio...