This paper presents a general variable neighborhood search (GVNS) heuristic for solving the maximum diverse grouping problem. Extensive computational experiments performed on a series of large random graphs as well as on several instances of the maximum diversity problem taken from the literature show that the results obtained by GVNS consistently outperform the best heuristics from the literat...

A significant part of the research on problems quantization random variables is devoted to practical aspects optimal in sense filling information. For these purposes, certain quantitative characteristics quantized are used, such as: mathematical expectation, variance and mean square deviation. At same time, determine variables, as a rule, well-known parametric distributions used: uniform, expon...

Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing are designed rank predictors according their individual contributions response. As a result, variables that marginally independent but jointly dependent with response could missed. In this work, we propose new framework for variable screening, random subspace...

Abstract Higher-order networks aim at improving the classical network representation of trajectories data as memory-less order $1$ Markov models. To do so, locations are associated with different representations or “memory nodes” representing indirect dependencies between visited places direct relations. One promising area investigation in this context is variable-order models it was suggested ...

We tackle the facial landmark localization problem as an inference problem over a Markov Random Field. Efficient inference is implemented using Gibbs sampling with approximated full conditional distributions in a latent variable model. This approximation allows us to improve the runtime performance 1000-fold over classical formulations with no perceptible loss in accuracy. The exceptional robus...