We show that the Kronecker sum of d >= 2 copies of a random one-dimensional sparse model displays a spectral transition of the type predicted by Anderson, from absolutely continuous around the center of the band to pure ...

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a learned dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art ...

In this paper we address the problem of sparse signal reconstruction. We propose a new algorithm that determines the signal support applying statistical thresholding to accept the active components of the model. This ...

We consider the problem of estimating a signal which has been corrupted with structured noise. When the signal of interest accepts a sparse representation, only a small number of measurements are required to retain all the ...

Penalized regression estimators are popular tools for the analysis of sparse and high-dimensional models. However, penalized regression estimators defined using an unbounded loss function can be very sensitive to the ...

Este trabalho consiste em três contribuições independentes do âmbito da modelagem cognitiva ao campo de management science. O primeiro aborda Experience Recognition, uma teoria inicialmente introduzida por Linhares e Freitas ...