6 accepted papers
In recent years, the interest in kernel methods has increased exponentially, mainly due to applications including phenomena that cannot be well modeled by linear systems. Furthermore, the demand for high-speed communications and improvement in computer capacity to process information leads to the ex…
Set-membership affine projection (SM-AP) adaptive filters have been increasingly employed in the context of online data-selective learning. A key aspect for their good performance in terms of both convergence speed and steady-state mean-squared error is the choice of the so-called constraint vector.…
The huge volume of data that are available today requires data-selective processing approaches that avoid the costs in computational complexity via appropriately treating the non-innovative data. In this paper, extensions of the well-known adaptive filtering LMS-Newton and LMS-Quasi-Newton Algorithm…
In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the pot…
In this paper, we propose some sparsity aware algorithms, namely the Recursive least-Squares for sparse systems (S-RLS) and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm Recursive least-Squares (l <sub xmlns:mml="http://www.w3.org/1998…
In this paper, we present an improved set-membership partial-update affine projection (I-SM-PUAP) algorithm, aiming at accelerating the convergence, and decreasing the update rates and the computational complexity of the set-membership partial-update affine projection (SM-PUAP) algorithm. To meet th…