l(1)-penalized least mean squarevariable-step-sizeadaptive filteringSummary Recently, sparsity-aware least mean square (LMS) algorithms have been proposed to improve the performance of the standard LMS algorithm
Variational Bayesian EM (VB-EM) algorithm, as addressed in Section 3.7.3, is applied here to derive the approximate solution to the Poisson–Gamma Bayesian NMF. This algorithm is implemented by performing the E and M steps of VB. The variational distributions of three latent variations are repr...
A sparse signal with support can be easily resolved using least squares optimization [69,70]. Show moreView chapter Book 2022, Deep Learning for Robot Perception and CognitionAysen Degerli, ... Moncef Gabbouj Chapter Advancements in Bayesian Methods and Implementation 1.1 Quick summary of existing ...
This paper presents an improved off-grid direction of arrival (DOA) estimation algorithm based on sparse Bayesian learning. The algorithm aims to enhance the accuracy of DOA estimation for coherent signals in EM environments with a small number of snapshots and a low SNR. The algorithm first ...
Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary comp
Our experimental results show that the proposed SSIM-based sparse representation algorithm achieves better SSIM performance and better visual quality than the corresponding least square-based method. 1 Introduction In many signal processing problems, mean squared error (MSE) has been the preferred choice...
(training) set, respectively, which are better than those of BM-SCCA. These results indicate that the search space of BM-SCCA could be too large such that the algorithm could converge to local optima without prior knowledge, while the regularizations of the restrictions on outcome-relevant ...
(without the use of numerical quadrature), which makes the method computationally feasible. a similar algorithm based on tensor-train approximations of the density has recently been proposed in [ 22 ]. 7.4.3 estimating the parameter in the bayesian setting, a natural point estimate of the ...
Finally, a Bayesian optimizer is used to automatically adjust the hyperparameters of the STLS algorithm, thereby improving the generalizability of the algorithm. Show abstract GPS multipath and NLOS mitigation for relative positioning in urban environments 2020, Aerospace Science and Technology Show ...
The K-SVD algorithm is inspired from the k-means clustering algorithm, which is also an NP-hard problem. The aim of k-means clustering is to partition all the signals into K clusters, in which each training signal belongs to the cluster with the nearest mean. It employs an iterative appro...