Weighted Sparse Bayesian Learning for Electrical Impedance Tomography (EIT) is a MATLAB code package designed to implement a sophisticated algorithm for EIT reconstruction. It utilizes a technique known as Bound Optimization to perform weighted sparse Bayesian learning, allowing for efficient parameterization...
In this study, we proposed a Gaussian weighted block sparse Bayesian learning strategy based on K-means clustering algorithm (GBSBLK) for accurate BLT reconstruction. GBSBLK integrated the structured sparsity assumption, the K-means clustering strategy, and the block sparse Bayesian learning (BSBL) ...
Adaptive regularisation variation model based on sparse representation Guan et al. (2020) Bayesian inference Sadeq et al. (2016) Maximum likelihood Jiang et al. (2014), Sadeq et al. (2016) Clustering approaches Fuss et al. (2016) Coherence weighted average, Maximum coherence, Coherence mixture ...
(2021). Linear convergence in federated learning: Tackling client heterogeneity and sparse gradients. In: NeurIPS. Nocedal, J., & Wright, S. (2006). Numerical optimization. New York: Springer Science & Business Media. MATH Google Scholar Nocedal, J., & Wright, S. J. (1999). Numerical ...
Particularly when analyzing, for example, elevation-temperature dependencies in areas of rather low relief energy, GWR may yield physically implausible lapse rates which, in case of sparse data coverage in adjacent mountainous areas, would serve to bias the spatialization results. More robust in this...
Tsybakov, A. (2003), ‘Optimal Rates of Aggregation’, inComputational Learning Theory and Kernel Machines, New York: Springer, pp. 303–313. Google Scholar Tsybakov, A., and Rigollet, P. (2011), ‘Exponential Screening and Optimal Rates of Sparse Estimation’,The Annals of Statistics, 39...
We model the appearance as a sparse linear combination of structured subspaces. Block orthogonal matching pursuit is used to reduce the computational comp... T Bai,YF Li - 《Pattern Recognition》 被引量: 180发表: 2012年 Efficient online structured output learning for keypoint-based object tracking...
Next, we sought to derive a Bayesian consensus WSBM from multiple fits of our data56. First, we aggregated the results of the 100 WSBM fits at our data-driven selectedk. Next, we choose a representative partition from these 100 fits by determining the partition least distant from all other...
combining semi-supervised learning, multiple instance learning and the Bayesian theorem. The tracker uses a block-based inconsistency function of the labeled and unlabeled training samples in the selection of optimal weak classifiers during the parameter updating phase of each frame. Experimental results ...
in some techniques, specific data value estimates may be created for unviewed content items. Alternately, or additionally, data value estimates may be stored as differences between pairs in a sparse matrix, which may facilitate the rapid calculation of data value estimates for newly added content ...