LZ Xu, KX Zhao, J Li, P Stoica, Wideband source localization using sparse learning via iterative minimization. Sig. Process. 93(12), 3504-3514 (2013)L. Xu, K. Zhao, J. Li, and P. Stoica, "Wideband source locali
[57] have proposed some new insights on automatic relevance determination and sparse Bayesian learning. They have shown that, for the vector regression case, ARD can be achieved by means of iterative reweighted ℓ1 minimization. Furthermore, in that paper, they have sketched an extension of ...
A sparse code inference aims at computing sparse codes for given data and is most widely addressed via iterative schemes such as aforementioned ISTA and FISTA. Predicting approximations of optimal codes can be done using deep feed-forward learning architectures based on truncated convex solvers. This...
pythonsparsityoptimizationcudaadmmsparse-codingdictionary-learningoptimization-algorithmsrobust-pcafistaconvolutional-sparse-codingtotal-variationsparse-representationsconvolutional-dictionary-learningtotal-variation-minimizationplug-and-play-priors UpdatedJan 17, 2025 ...
Valuable prior appearance of tracking object obtained through the second camera is integrated into an augmented dictionary via the proposed crossover templates. The depth is integrated into the sparse learning framework in three aspects. First, an extra depth view is added to the color image-based ...
We proposed using a sparse, quantized neural code to deal with noisy and partial inputs and to prevent catastrophic forgetting, and implementing this strategy via a discrete graphical model that performed MAP learning, an algorithm that uses local learning rules. We implemented this approach in the...
稀疏贝叶斯学习(SparseBayesianLearning) 稀疏贝叶斯学习(Sparse Bayesian Learning) 张智林(Zhilin?Zhang)? z4zhang@ Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093-0407, USA 1 引言 稀疏贝叶斯学习(Sparse Bayesian Learning, SBL)最初作为一种机器...
In this paper, we improve a statistical iterative algorithm based on the minimization of the image total variation (TV) for sparse or limited projection views during CT image reconstruction. Considering the statistical nature of the projection data, the TV is performed under a penalized weighted ...
6.2.2 Dictionary Learning Problem In sparse coding, it is assumed that the overcomplete dictionary D is given or known a priori. The dictionary can be directly chosen as a set of training signals or a prespecified basis such as overcomplete wavelets, curvelets, contourlets, and short-time Four...
Adaptive multiple graph regularized semi-supervised extreme learning machine. Soft Comput. 2018;22(11):3545–62. 48. Boyd S, Vandenberghe L. Convex optimization. Cambridge: Cambridge University Press; 2004. 49. He R, Zheng W-S, Tan T, Sun Z. Half-quadratic-based iterative minimization for ...