Xu Lu-zhou, Zhao Ke-xin, Li Jian, et al.. Wideband source localization using sparse learning via iterative minimization [J]. Signal Processing, 2013, 93(12): 3504-3514.L. Xu, K. Zhao, J. Li, and P. Stoica, "Wideband source localization using sparse 354 learning via iterative ...
LZ Xu, KX Zhao, J Li, P Stoica, Wideband source localization using sparse learning via iterative minimization. Sig. Process. 93(12), 3504–3514 (2013). Article Google Scholar ZQ He, ZP Shi, L Huang, HC So, Underdetermined DOA estimation for wideband signals using robust sparse covariance...
[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 ...
pythonsparsityoptimizationcudaadmmsparse-codingdictionary-learningoptimization-algorithmsrobust-pcafistaconvolutional-sparse-codingtotal-variationsparse-representationsconvolutional-dictionary-learningtotal-variation-minimizationplug-and-play-priors UpdatedJan 17, 2025 ...
A. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods 16, 1215–1225 (2019). Article CAS PubMed Google Scholar Gu, L. et al. Molecular resolution imaging by repetitive optical selective exposure. Nat. Methods 16, 1114–1118 (2019). ...
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 ...
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 addition, we employ a template update strategy which combines incremental subspace learning and sparse representation. 此外,我们采用了一种结合增量子空间学习和稀疏表示的模版更新策略。 This strategy adapts the template to the appearance change of the target with less possibility of drifting and reduces...
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...
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...