Abstract: In this paper we propose a Maximum a Posteriori (MAP) approach for estimating a random sparse parameter vector in the presence of nonlinearities ...
scholar.google.com › citations
Abstract— In this paper we propose a Maximum a Posteriori. (MAP) approach for estimating a random sparse parameter vector in the presence of nonlinearities ...
This paper solves the identification problem by using a generalized Expectation Maximization algorithm in a MAP framework for estimating a random sparse ...
In this paper we propose a Maximum a Posteriori (MAP) approach for estimating a random sparse parameter vector in the presence of nonlinearities of unknown ...
This paper addresses the problem of estimating sparse communication channels in OFDM systems by maximizing a regularized (modified) likelihood function, ...
Dec 10, 2023 · These adaptive algorithms utilize the results from the least absolute shrinkage and selection operator (LASSO) [3] and compressive sensing ...
This paper considers the use of sparse estimation techniques to determine an appropriate set of basis functions, in terms of the number of poles and their ...
An EM-based estimation algorithm for a class of systems promoting sparsity. - Godoy, Boris I., Carvajal, Rodrigo, Agüero, Juan C. Creator ...
Dec 10, 2023 · In this paper, the recursive least squares (RLS) algorithm is considered in the sparse system identifi- cation setting.
Popular convex approaches for sparse estimation such as Lasso and Multiple Kernel Learning (MKL) can be derived in a Bayesian setting, starting from a ...