sparse Bayesian learningsparse overcomplete codingImages can be coded accurately using a sparse set of vectors from a learned overcomplete dictionary, with potential applications in image compression and feature
11.5.1.3 Sparse Bayesian learning A systematic approach to off-grid DOA estimation, called off-grid sparse Bayesian inference (OGSBI), was proposed in [104] within the framework of SBL in the multiple snapshot case. In order to estimate the additional parameter β, it is assumed that βn,...
Code Issues Pull requests Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian learning, and basis pursuit.
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 order to solve this problem, compressive sensing (CS) is applied to NBI mitigation in DSSS communications, the impact of NBI on the reconstruction of the DSSS signal after compressed sampling is analyzed, and a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL)...
Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211–244. [Abstract] [Available from JMLR] There are a couple of minor typos in the above paper. Two early conference publications on the Relevance Vector Machine:...
Fan X, Yuan C, Malone B (2014) Tightening bounds for Bayesian network structure learning. AAAI 4:2439–2445 Google Scholar Gao S, Tsang IW, Chia LT, Zhao P (2010) Local features are not lonely—Laplacian sparse coding for image classification. In: CVPR, pp 3555–3561 Georghiades A, ...
sparsitycompressed-sensingjuliafeature-selectionsparse-linear-systemssparse-regressionmatching-pursuitsparse-bayesian-learningstepwise-regressionsubset-selectionbasis-pursuit UpdatedMar 28, 2022 Julia Hua-Zhou/SparseReg Star26 Code Issues Pull requests Matlab toolbox for sparse regression ...
The best-representing region for each input low-resolution patch in the input image is found through an efficient Local Naive Bayesian selection stage on SIFT features. The resulting adapted subset of patches is then compressed using the standard sparse coding techniques and used for super resolution...
Bayesian inference Neural network Partial differential equation Inverse problems 1. Introduction In recent years, pioneering research has been conducted into the application of machine learning to computational physics and engineering contexts: example works include [1], [2], [3], [4], [5], [6]...