Webet al. have recently proposed a user parameter-free SParse Iterative Covariance-based Estimation (SPICE) approach in [20], [21] based on minimizing a covariance matrix fitting criterion. However, the SPICE approach proposed in [20] for the multiple-snapshot case depends on the inverse of the sample covariance matrix, which exists only if WebA popular approach to covariance selection has been to maximize an ‘ 1 penalized log likelihood objective, [4]. This approach has also been applied to other highly related problems such as sparse multivariate regression with co-variance estimation, [5], and covariance selection under a Kronecker product structure, [6]. In this
[PDF] SPICE: A Sparse Covariance-Based Estimation Method for …
WebA novel algorithm for high-resolution ISAR imaging based on the SParse Iterative Covariance-based Estimation (SPICE) is proposed, which does not need to set any parameters and it converges globally, so it can realize high quality imaging with limited measurements. High-resolution of Inverse Synthetic Aperture Radar (ISAR) in the azimuth … Web6. apr 2024 · Covariance function estimation is a fundamental task in multivariate functional data analysis and arises in many applications. In this paper, we consider estimating sparse covariance functions for ... medilec perth
An Iterative Lq-norm Based Optimization Algorithm for
WebA popular approach to covariance selection has been to maximize an ‘ 1 penalized log likelihood objective, [4]. This approach has also been applied to other highly related problems such as sparse multivariate regression with co-variance estimation, [5], and covariance selection under a Kronecker product structure, [6]. In this WebMany popular sparse estimation methods are based on reg-ularizing the least-squares method by penalizing a norm of the parameter vector x, in an attempt to strike a balance between data fidelity and parameter sparsity. While such sparsifying methods can estimate x in highly underdetermined scenarios, WebMentioning: 2 - An off-grid sparse direction-of-arrival (DOA) estimation algorithm, namely, iterative reweighted linear interpolation (IRLI), is proposed to avoid the declination of the DOA estimation precision present in unknown spatial coloured noise. The authors start by developing an off-grid sparse model based on linear interpolation with reweighted … medina city holiday trash pickup